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What field is the article from? | Title: Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice
Abstract: When systems use data-based models that are based on machine learning (ML),
errors in their results cannot be ruled out. This is particularly critical if
it remains unclear to the user how these models arrived at their decisions and
if errors can have safety-relevant consequences, as is often the case in the
medical field. In such cases, the use of dependable methods to quantify the
uncertainty remaining in a result allows the user to make an informed decision
about further usage and draw possible conclusions based on a given result. This
paper demonstrates the applicability and practical utility of the Uncertainty
Wrapper using flow cytometry as an application from the medical field that can
benefit from the use of ML models in conjunction with dependable and
transparent uncertainty quantification. | Machine Learning |
What field is the article from? | Title: 1D-Convolutional transformer for Parkinson disease diagnosis from gait
Abstract: This paper presents an efficient deep neural network model for diagnosing
Parkinson's disease from gait. More specifically, we introduce a hybrid
ConvNet-Transformer architecture to accurately diagnose the disease by
detecting the severity stage. The proposed architecture exploits the strengths
of both Convolutional Neural Networks and Transformers in a single end-to-end
model, where the former is able to extract relevant local features from
Vertical Ground Reaction Force (VGRF) signal, while the latter allows to
capture long-term spatio-temporal dependencies in data. In this manner, our
hybrid architecture achieves an improved performance compared to using either
models individually. Our experimental results show that our approach is
effective for detecting the different stages of Parkinson's disease from gait
data, with a final accuracy of 88%, outperforming other state-of-the-art AI
methods on the Physionet gait dataset. Moreover, our method can be generalized
and adapted for other classification problems to jointly address the feature
relevance and spatio-temporal dependency problems in 1D signals. Our source
code and pre-trained models are publicly available at
https://github.com/SafwenNaimi/1D-Convolutional-transformer-for-Parkinson-disease-diagnosis-from-gait. | Computer Vision |
What field is the article from? | Title: Outlier Dimensions Encode Task-Specific Knowledge
Abstract: Representations from large language models (LLMs) are known to be dominated
by a small subset of dimensions with exceedingly high variance. Previous works
have argued that although ablating these outlier dimensions in LLM
representations hurts downstream performance, outlier dimensions are
detrimental to the representational quality of embeddings. In this study, we
investigate how fine-tuning impacts outlier dimensions and show that 1) outlier
dimensions that occur in pre-training persist in fine-tuned models and 2) a
single outlier dimension can complete downstream tasks with a minimal error
rate. Our results suggest that outlier dimensions can encode crucial
task-specific knowledge and that the value of a representation in a single
outlier dimension drives downstream model decisions. | Computational Linguistics |
What field is the article from? | Title: United We Stand, Divided We Fall: UnityGraph for Unsupervised Procedure Learning from Videos
Abstract: Given multiple videos of the same task, procedure learning addresses
identifying the key-steps and determining their order to perform the task. For
this purpose, existing approaches use the signal generated from a pair of
videos. This makes key-steps discovery challenging as the algorithms lack
inter-videos perspective. Instead, we propose an unsupervised Graph-based
Procedure Learning (GPL) framework. GPL consists of the novel UnityGraph that
represents all the videos of a task as a graph to obtain both intra-video and
inter-videos context. Further, to obtain similar embeddings for the same
key-steps, the embeddings of UnityGraph are updated in an unsupervised manner
using the Node2Vec algorithm. Finally, to identify the key-steps, we cluster
the embeddings using KMeans. We test GPL on benchmark ProceL, CrossTask, and
EgoProceL datasets and achieve an average improvement of 2% on third-person
datasets and 3.6% on EgoProceL over the state-of-the-art. | Computer Vision |
What field is the article from? | Title: How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors?
Abstract: Surging interest in deep learning from high-stakes domains has precipitated
concern over the inscrutable nature of black box neural networks. Explainable
AI (XAI) research has led to an abundance of explanation algorithms for these
black boxes. Such post hoc explainers produce human-comprehensible
explanations, however, their fidelity with respect to the model is not well
understood - explanation evaluation remains one of the most challenging issues
in XAI. In this paper, we ask a targeted but important question: can popular
feature-additive explainers (e.g., LIME, SHAP, SHAPR, MAPLE, and PDP) explain
feature-additive predictors? Herein, we evaluate such explainers on ground
truth that is analytically derived from the additive structure of a model. We
demonstrate the efficacy of our approach in understanding these explainers
applied to symbolic expressions, neural networks, and generalized additive
models on thousands of synthetic and several real-world tasks. Our results
suggest that all explainers eventually fail to correctly attribute the
importance of features, especially when a decision-making process involves
feature interactions. | Machine Learning |
What field is the article from? | Title: Context-aware explainable recommendations over knowledge graphs
Abstract: Knowledge graphs contain rich semantic relationships related to items and
incorporating such semantic relationships into recommender systems helps to
explore the latent connections of items, thus improving the accuracy of
prediction and enhancing the explainability of recommendations. However, such
explainability is not adapted to users' contexts, which can significantly
influence their preferences. In this work, we propose CA-KGCN (Context-Aware
Knowledge Graph Convolutional Network), an end-to-end framework that can model
users' preferences adapted to their contexts and can incorporate rich semantic
relationships in the knowledge graph related to items. This framework captures
users' attention to different factors: contexts and features of items. More
specifically, the framework can model users' preferences adapted to their
contexts and provide explanations adapted to the given context. Experiments on
three real-world datasets show the effectiveness of our framework: modeling
users' preferences adapted to their contexts and explaining the recommendations
generated. | Information Retrieval |
What field is the article from? | Title: Soil Organic Carbon Estimation from Climate-related Features with Graph Neural Network
Abstract: Soil organic carbon (SOC) plays a pivotal role in the global carbon cycle,
impacting climate dynamics and necessitating accurate estimation for
sustainable land and agricultural management. While traditional methods of SOC
estimation face resolution and accuracy challenges, recent technological
solutions harness remote sensing, machine learning, and high-resolution
satellite mapping. Graph Neural Networks (GNNs), especially when integrated
with positional encoders, can capture complex relationships between soil and
climate. Using the LUCAS database, this study compared four GNN operators in
the positional encoder framework. Results revealed that the PESAGE and
PETransformer models outperformed others in SOC estimation, indicating their
potential in capturing the complex relationship between SOC and climate
features. Our findings confirm the feasibility of applications of GNN
architectures in SOC prediction, establishing a framework for future
explorations of this topic with more advanced GNN models. | Machine Learning |
What field is the article from? | Title: Training Dynamics of Contextual N-Grams in Language Models
Abstract: Prior work has shown the existence of contextual neurons in language models,
including a neuron that activates on German text. We show that this neuron
exists within a broader contextual n-gram circuit: we find late layer neurons
which recognize and continue n-grams common in German text, but which only
activate if the German neuron is active. We investigate the formation of this
circuit throughout training and find that it is an example of what we call a
second-order circuit. In particular, both the constituent n-gram circuits and
the German detection circuit which culminates in the German neuron form with
independent functions early in training - the German detection circuit
partially through modeling German unigram statistics, and the n-grams by
boosting appropriate completions. Only after both circuits have already formed
do they fit together into a second-order circuit. Contrary to the hypotheses
presented in prior work, we find that the contextual n-gram circuit forms
gradually rather than in a sudden phase transition. We further present a range
of anomalous observations such as a simultaneous phase transition in many tasks
coinciding with the learning rate warm-up, and evidence that many context
neurons form simultaneously early in training but are later unlearned. | Machine Learning |
What field is the article from? | Title: Ontology Learning Using Formal Concept Analysis and WordNet
Abstract: Manual ontology construction takes time, resources, and domain specialists.
Supporting a component of this process for automation or semi-automation would
be good. This project and dissertation provide a Formal Concept Analysis and
WordNet framework for learning concept hierarchies from free texts. The process
has steps. First, the document is Part-Of-Speech labeled, then parsed to
produce sentence parse trees. Verb/noun dependencies are derived from parse
trees next. After lemmatizing, pruning, and filtering the word pairings, the
formal context is created. The formal context may contain some erroneous and
uninteresting pairs because the parser output may be erroneous, not all derived
pairs are interesting, and it may be large due to constructing it from a large
free text corpus. Deriving lattice from the formal context may take longer,
depending on the size and complexity of the data. Thus, decreasing formal
context may eliminate erroneous and uninteresting pairs and speed up idea
lattice derivation. WordNet-based and Frequency-based approaches are tested.
Finally, we compute formal idea lattice and create a classical concept
hierarchy. The reduced concept lattice is compared to the original to evaluate
the outcomes. Despite several system constraints and component discrepancies
that may prevent logical conclusion, the following data imply idea hierarchies
in this project and dissertation are promising. First, the reduced idea lattice
and original concept have commonalities. Second, alternative language or
statistical methods can reduce formal context size. Finally, WordNet-based and
Frequency-based approaches reduce formal context differently, and the order of
applying them is examined to reduce context efficiently. | Computational Linguistics |
What field is the article from? | Title: Course Correcting Koopman Representations
Abstract: Koopman representations aim to learn features of nonlinear dynamical systems
(NLDS) which lead to linear dynamics in the latent space. Theoretically, such
features can be used to simplify many problems in modeling and control of NLDS.
In this work we study autoencoder formulations of this problem, and different
ways they can be used to model dynamics, specifically for future state
prediction over long horizons. We discover several limitations of predicting
future states in the latent space and propose an inference-time mechanism,
which we refer to as Periodic Reencoding, for faithfully capturing long term
dynamics. We justify this method both analytically and empirically via
experiments in low and high dimensional NLDS. | Machine Learning |
What field is the article from? | Title: A Survey of Adversarial CAPTCHAs on its History, Classification and Generation
Abstract: Completely Automated Public Turing test to tell Computers and Humans Apart,
short for CAPTCHA, is an essential and relatively easy way to defend against
malicious attacks implemented by bots. The security and usability trade-off
limits the use of massive geometric transformations to interfere deep model
recognition and deep models even outperformed humans in complex CAPTCHAs. The
discovery of adversarial examples provides an ideal solution to the security
and usability trade-off by integrating adversarial examples and CAPTCHAs to
generate adversarial CAPTCHAs that can fool the deep models. In this paper, we
extend the definition of adversarial CAPTCHAs and propose a classification
method for adversarial CAPTCHAs. Then we systematically review some commonly
used methods to generate adversarial examples and methods that are successfully
used to generate adversarial CAPTCHAs. Also, we analyze some defense methods
that can be used to defend adversarial CAPTCHAs, indicating potential threats
to adversarial CAPTCHAs. Finally, we discuss some possible future research
directions for adversarial CAPTCHAs at the end of this paper. | Cryptography and Security |
What field is the article from? | Title: Safety-aware Causal Representation for Trustworthy Reinforcement Learning in Autonomous Driving
Abstract: In the domain of autonomous driving, the Learning from Demonstration (LfD)
paradigm has exhibited notable efficacy in addressing sequential
decision-making problems. However, consistently achieving safety in varying
traffic contexts, especially in safety-critical scenarios, poses a significant
challenge due to the long-tailed and unforeseen scenarios absent from offline
datasets. In this paper, we introduce the saFety-aware strUctured Scenario
representatION (FUSION), a pioneering methodology conceived to facilitate the
learning of an adaptive end-to-end driving policy by leveraging structured
scenario information. FUSION capitalizes on the causal relationships between
decomposed reward, cost, state, and action space, constructing a framework for
structured sequential reasoning under dynamic traffic environments. We conduct
rigorous evaluations in two typical real-world settings of distribution shift
in autonomous vehicles, demonstrating the good balance between safety cost and
utility reward of FUSION compared to contemporary state-of-the-art safety-aware
LfD baselines. Empirical evidence under diverse driving scenarios attests that
FUSION significantly enhances the safety and generalizability of autonomous
driving agents, even in the face of challenging and unseen environments.
Furthermore, our ablation studies reveal noticeable improvements in the
integration of causal representation into the safe offline RL problem. | Robotics |
What field is the article from? | Title: Emu Edit: Precise Image Editing via Recognition and Generation Tasks
Abstract: Instruction-based image editing holds immense potential for a variety of
applications, as it enables users to perform any editing operation using a
natural language instruction. However, current models in this domain often
struggle with accurately executing user instructions. We present Emu Edit, a
multi-task image editing model which sets state-of-the-art results in
instruction-based image editing. To develop Emu Edit we train it to multi-task
across an unprecedented range of tasks, such as region-based editing, free-form
editing, and Computer Vision tasks, all of which are formulated as generative
tasks. Additionally, to enhance Emu Edit's multi-task learning abilities, we
provide it with learned task embeddings which guide the generation process
towards the correct edit type. Both these elements are essential for Emu Edit's
outstanding performance. Furthermore, we show that Emu Edit can generalize to
new tasks, such as image inpainting, super-resolution, and compositions of
editing tasks, with just a few labeled examples. This capability offers a
significant advantage in scenarios where high-quality samples are scarce.
Lastly, to facilitate a more rigorous and informed assessment of instructable
image editing models, we release a new challenging and versatile benchmark that
includes seven different image editing tasks. | Computer Vision |
What field is the article from? | Title: Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning
Abstract: In the field of explainable Artificial Intelligence (XAI), sequential
counterfactual (SCF) examples are often used to alter the decision of a trained
classifier by implementing a sequence of modifications to the input instance.
Although certain test-time algorithms aim to optimize for each new instance
individually, recently Reinforcement Learning (RL) methods have been proposed
that seek to learn policies for discovering SCFs, thereby enhancing
scalability. As is typical in RL, the formulation of the RL problem, including
the specification of state space, actions, and rewards, can often be ambiguous.
In this work, we identify shortcomings in existing methods that can result in
policies with undesired properties, such as a bias towards specific actions. We
propose to use the output probabilities of the classifier to create a more
informative reward, to mitigate this effect. | Machine Learning |
What field is the article from? | Title: Concept-free Causal Disentanglement with Variational Graph Auto-Encoder
Abstract: In disentangled representation learning, the goal is to achieve a compact
representation that consists of all interpretable generative factors in the
observational data. Learning disentangled representations for graphs becomes
increasingly important as graph data rapidly grows. Existing approaches often
rely on Variational Auto-Encoder (VAE) or its causal structure learning-based
refinement, which suffer from sub-optimality in VAEs due to the independence
factor assumption and unavailability of concept labels, respectively. In this
paper, we propose an unsupervised solution, dubbed concept-free causal
disentanglement, built on a theoretically provable tight upper bound
approximating the optimal factor. This results in an SCM-like causal structure
modeling that directly learns concept structures from data. Based on this idea,
we propose Concept-free Causal VGAE (CCVGAE) by incorporating a novel causal
disentanglement layer into Variational Graph Auto-Encoder. Furthermore, we
prove concept consistency under our concept-free causal disentanglement
framework, hence employing it to enhance the meta-learning framework, called
concept-free causal Meta-Graph (CC-Meta-Graph). We conduct extensive
experiments to demonstrate the superiority of the proposed models: CCVGAE and
CC-Meta-Graph, reaching up to $29\%$ and $11\%$ absolute improvements over
baselines in terms of AUC, respectively. | Machine Learning |
What field is the article from? | Title: Digital Socrates: Evaluating LLMs through explanation critiques
Abstract: While LLMs can provide reasoned explanations along with their answers, the
nature and quality of those explanations are still poorly understood. In
response, our goal is to define a detailed way of characterizing the
explanation capabilities of modern models and to create a nuanced,
interpretable explanation evaluation tool that can generate such
characterizations automatically, without relying on expensive API calls or
human annotations. Our approach is to (a) define the new task of explanation
critiquing - identifying and categorizing any main flaw in an explanation and
providing suggestions to address the flaw, (b) create a sizeable,
human-verified dataset for this task, and (c) train an open-source, automatic
critiquing model (called Digital Socrates) using this data. Through
quantitative and qualitative analysis, we demonstrate how Digital Socrates is
useful for revealing insights about student models by examining their reasoning
chains, and how it can provide high-quality, nuanced, automatic evaluation of
those model explanations for the first time. Digital Socrates thus fills an
important gap in evaluation tools for understanding and improving the
explanation behavior of models. | Computational Linguistics |
What field is the article from? | Title: Constant-time Motion Planning with Anytime Refinement for Manipulation
Abstract: Robotic manipulators are essential for future autonomous systems, yet limited
trust in their autonomy has confined them to rigid, task-specific systems. The
intricate configuration space of manipulators, coupled with the challenges of
obstacle avoidance and constraint satisfaction, often makes motion planning the
bottleneck for achieving reliable and adaptable autonomy. Recently, a class of
constant-time motion planners (CTMP) was introduced. These planners employ a
preprocessing phase to compute data structures that enable online planning
provably guarantee the ability to generate motion plans, potentially
sub-optimal, within a user defined time bound. This framework has been
demonstrated to be effective in a number of time-critical tasks. However,
robotic systems often have more time allotted for planning than the online
portion of CTMP requires, time that can be used to improve the solution. To
this end, we propose an anytime refinement approach that works in combination
with CTMP algorithms. Our proposed framework, as it operates as a constant time
algorithm, rapidly generates an initial solution within a user-defined time
threshold. Furthermore, functioning as an anytime algorithm, it iteratively
refines the solution's quality within the allocated time budget. This enables
our approach to strike a balance between guaranteed fast plan generation and
the pursuit of optimization over time. We support our approach by elucidating
its analytical properties, showing the convergence of the anytime component
towards optimal solutions. Additionally, we provide empirical validation
through simulation and real-world demonstrations on a 6 degree-of-freedom robot
manipulator, applied to an assembly domain. | Robotics |
What field is the article from? | Title: Interpretable Prototype-based Graph Information Bottleneck
Abstract: The success of Graph Neural Networks (GNNs) has led to a need for
understanding their decision-making process and providing explanations for
their predictions, which has given rise to explainable AI (XAI) that offers
transparent explanations for black-box models. Recently, the use of prototypes
has successfully improved the explainability of models by learning prototypes
to imply training graphs that affect the prediction. However, these approaches
tend to provide prototypes with excessive information from the entire graph,
leading to the exclusion of key substructures or the inclusion of irrelevant
substructures, which can limit both the interpretability and the performance of
the model in downstream tasks. In this work, we propose a novel framework of
explainable GNNs, called interpretable Prototype-based Graph Information
Bottleneck (PGIB) that incorporates prototype learning within the information
bottleneck framework to provide prototypes with the key subgraph from the input
graph that is important for the model prediction. This is the first work that
incorporates prototype learning into the process of identifying the key
subgraphs that have a critical impact on the prediction performance. Extensive
experiments, including qualitative analysis, demonstrate that PGIB outperforms
state-of-the-art methods in terms of both prediction performance and
explainability. | Machine Learning |
What field is the article from? | Title: Dynamic Corrective Self-Distillation for Better Fine-Tuning of Pretrained Models
Abstract: We tackle the challenging issue of aggressive fine-tuning encountered during
the process of transfer learning of pre-trained language models (PLMs) with
limited labeled downstream data. This problem primarily results in a decline in
performance on the subsequent task. Inspired by the adaptive boosting method in
traditional machine learning, we present an effective dynamic corrective
self-distillation (DCS) approach to improve the fine-tuning of the PLMs. Our
technique involves performing a self-distillation mechanism where, at each
iteration, the student model actively adapts and corrects itself by dynamically
adjusting the weights assigned to individual data points. This iterative
self-correcting process significantly enhances the overall fine-tuning
capability of PLMs, leading to improved performance and robustness. We
conducted comprehensive evaluations using the GLUE benchmark demonstrating the
efficacy of our method in enhancing the fine-tuning process for various PLMs
across diverse downstream tasks. | Computational Linguistics |
What field is the article from? | Title: OC-NMN: Object-centric Compositional Neural Module Network for Generative Visual Analogical Reasoning
Abstract: A key aspect of human intelligence is the ability to imagine -- composing
learned concepts in novel ways -- to make sense of new scenarios. Such capacity
is not yet attained for machine learning systems. In this work, in the context
of visual reasoning, we show how modularity can be leveraged to derive a
compositional data augmentation framework inspired by imagination. Our method,
denoted Object-centric Compositional Neural Module Network (OC-NMN), decomposes
visual generative reasoning tasks into a series of primitives applied to
objects without using a domain-specific language. We show that our modular
architectural choices can be used to generate new training tasks that lead to
better out-of-distribution generalization. We compare our model to existing and
new baselines in proposed visual reasoning benchmark that consists of applying
arithmetic operations to MNIST digits. | Artificial Intelligence |
What field is the article from? | Title: Learn to Optimize Denoising Scores for 3D Generation: A Unified and Improved Diffusion Prior on NeRF and 3D Gaussian Splatting
Abstract: We propose a unified framework aimed at enhancing the diffusion priors for 3D
generation tasks. Despite the critical importance of these tasks, existing
methodologies often struggle to generate high-caliber results. We begin by
examining the inherent limitations in previous diffusion priors. We identify a
divergence between the diffusion priors and the training procedures of
diffusion models that substantially impairs the quality of 3D generation. To
address this issue, we propose a novel, unified framework that iteratively
optimizes both the 3D model and the diffusion prior. Leveraging the different
learnable parameters of the diffusion prior, our approach offers multiple
configurations, affording various trade-offs between performance and
implementation complexity. Notably, our experimental results demonstrate that
our method markedly surpasses existing techniques, establishing new
state-of-the-art in the realm of text-to-3D generation. Furthermore, our
approach exhibits impressive performance on both NeRF and the newly introduced
3D Gaussian Splatting backbones. Additionally, our framework yields insightful
contributions to the understanding of recent score distillation methods, such
as the VSD and DDS loss. | Computer Vision |
What field is the article from? | Title: COOL: A Constraint Object-Oriented Logic Programming Language and its Neural-Symbolic Compilation System
Abstract: This paper explores the integration of neural networks with logic
programming, addressing the longstanding challenges of combining the
generalization and learning capabilities of neural networks with the precision
of symbolic logic. Traditional attempts at this integration have been hampered
by difficulties in initial data acquisition, the reliability of undertrained
networks, and the complexity of reusing and augmenting trained models. To
overcome these issues, we introduce the COOL (Constraint Object-Oriented Logic)
programming language, an innovative approach that seamlessly combines logical
reasoning with neural network technologies. COOL is engineered to autonomously
handle data collection, mitigating the need for user-supplied initial data. It
incorporates user prompts into the coding process to reduce the risks of
undertraining and enhances the interaction among models throughout their
lifecycle to promote the reuse and augmentation of networks. Furthermore, the
foundational principles and algorithms in COOL's design and its compilation
system could provide valuable insights for future developments in programming
languages and neural network architectures. | Artificial Intelligence |
What field is the article from? | Title: Self Generated Wargame AI: Double Layer Agent Task Planning Based on Large Language Model
Abstract: The large language models represented by ChatGPT have a disruptive impact on
the field of artificial intelligence. But it mainly focuses on natural language
processing, speech recognition, machine learning and natural language
understanding. This paper innovatively applies the large language model to the
field of intelligent decision-making, places the large language model in the
decision-making center, and constructs an agent architecture with the large
language model as the core. Based on this, it further proposes a two-layer
agent task planning, issues and executes decision commands through the
interaction of natural language, and carries out simulation verification
through the wargame simulation environment. Through the game confrontation
simulation experiment, it is found that the intelligent decision-making ability
of the large language model is significantly stronger than the commonly used
reinforcement learning AI and rule AI, and the intelligence, understandability
and generalization are all better. And through experiments, it was found that
the intelligence of the large language model is closely related to prompt. This
work also extends the large language model from previous human-computer
interaction to the field of intelligent decision-making, which has important
reference value and significance for the development of intelligent
decision-making. | Artificial Intelligence |
What field is the article from? | Title: Bi-directional Adapter for Multi-modal Tracking
Abstract: Due to the rapid development of computer vision, single-modal (RGB) object
tracking has made significant progress in recent years. Considering the
limitation of single imaging sensor, multi-modal images (RGB, Infrared, etc.)
are introduced to compensate for this deficiency for all-weather object
tracking in complex environments. However, as acquiring sufficient multi-modal
tracking data is hard while the dominant modality changes with the open
environment, most existing techniques fail to extract multi-modal complementary
information dynamically, yielding unsatisfactory tracking performance. To
handle this problem, we propose a novel multi-modal visual prompt tracking
model based on a universal bi-directional adapter, cross-prompting multiple
modalities mutually. Our model consists of a universal bi-directional adapter
and multiple modality-specific transformer encoder branches with sharing
parameters. The encoders extract features of each modality separately by using
a frozen pre-trained foundation model. We develop a simple but effective light
feature adapter to transfer modality-specific information from one modality to
another, performing visual feature prompt fusion in an adaptive manner. With
adding fewer (0.32M) trainable parameters, our model achieves superior tracking
performance in comparison with both the full fine-tuning methods and the prompt
learning-based methods. Our code is available:
https://github.com/SparkTempest/BAT. | Computer Vision |
What field is the article from? | Title: Joint-Individual Fusion Structure with Fusion Attention Module for Multi-Modal Skin Cancer Classification
Abstract: Most convolutional neural network (CNN) based methods for skin cancer
classification obtain their results using only dermatological images. Although
good classification results have been shown, more accurate results can be
achieved by considering the patient's metadata, which is valuable clinical
information for dermatologists. Current methods only use the simple joint
fusion structure (FS) and fusion modules (FMs) for the multi-modal
classification methods, there still is room to increase the accuracy by
exploring more advanced FS and FM. Therefore, in this paper, we design a new
fusion method that combines dermatological images (dermoscopy images or
clinical images) and patient metadata for skin cancer classification from the
perspectives of FS and FM. First, we propose a joint-individual fusion (JIF)
structure that learns the shared features of multi-modality data and preserves
specific features simultaneously. Second, we introduce a fusion attention (FA)
module that enhances the most relevant image and metadata features based on
both the self and mutual attention mechanism to support the decision-making
pipeline. We compare the proposed JIF-MMFA method with other state-of-the-art
fusion methods on three different public datasets. The results show that our
JIF-MMFA method improves the classification results for all tested CNN
backbones and performs better than the other fusion methods on the three public
datasets, demonstrating our method's effectiveness and robustness | Computer Vision |
What field is the article from? | Title: Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots
Abstract: In the field of natural language processing, the prevalent approach involves
fine-tuning pretrained language models (PLMs) using local samples. Recent
research has exposed the susceptibility of PLMs to backdoor attacks, wherein
the adversaries can embed malicious prediction behaviors by manipulating a few
training samples. In this study, our objective is to develop a
backdoor-resistant tuning procedure that yields a backdoor-free model, no
matter whether the fine-tuning dataset contains poisoned samples. To this end,
we propose and integrate a honeypot module into the original PLM, specifically
designed to absorb backdoor information exclusively. Our design is motivated by
the observation that lower-layer representations in PLMs carry sufficient
backdoor features while carrying minimal information about the original tasks.
Consequently, we can impose penalties on the information acquired by the
honeypot module to inhibit backdoor creation during the fine-tuning process of
the stem network. Comprehensive experiments conducted on benchmark datasets
substantiate the effectiveness and robustness of our defensive strategy.
Notably, these results indicate a substantial reduction in the attack success
rate ranging from 10\% to 40\% when compared to prior state-of-the-art methods. | Machine Learning |
What field is the article from? | Title: Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt
Abstract: In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search
(L2S) solver for routing problems. It learns to perform flexible k-opt
exchanges based on a tailored action factorization method and a customized
recurrent dual-stream decoder. As a pioneering work to circumvent the pure
feasibility masking scheme and enable the autonomous exploration of both
feasible and infeasible regions, we then propose the Guided Infeasible Region
Exploration (GIRE) scheme, which supplements the NeuOpt policy network with
feasibility-related features and leverages reward shaping to steer
reinforcement learning more effectively. Additionally, we equip NeuOpt with
Dynamic Data Augmentation (D2A) for more diverse searches during inference.
Extensive experiments on the Traveling Salesman Problem (TSP) and Capacitated
Vehicle Routing Problem (CVRP) demonstrate that our NeuOpt not only
significantly outstrips existing (masking-based) L2S solvers, but also
showcases superiority over the learning-to-construct (L2C) and
learning-to-predict (L2P) solvers. Notably, we offer fresh perspectives on how
neural solvers can handle VRP constraints. Our code is available:
https://github.com/yining043/NeuOpt. | Machine Learning |
What field is the article from? | Title: Adversarial Estimation of Topological Dimension with Harmonic Score Maps
Abstract: Quantification of the number of variables needed to locally explain complex
data is often the first step to better understanding it. Existing techniques
from intrinsic dimension estimation leverage statistical models to glean this
information from samples within a neighborhood. However, existing methods often
rely on well-picked hyperparameters and ample data as manifold dimension and
curvature increases. Leveraging insight into the fixed point of the score
matching objective as the score map is regularized by its Dirichlet energy, we
show that it is possible to retrieve the topological dimension of the manifold
learned by the score map. We then introduce a novel method to measure the
learned manifold's topological dimension (i.e., local intrinsic dimension)
using adversarial attacks, thereby generating useful interpretations of the
learned manifold. | Machine Learning |
What field is the article from? | Title: A Novel Dataset for Financial Education Text Simplification in Spanish
Abstract: Text simplification, crucial in natural language processing, aims to make
texts more comprehensible, particularly for specific groups like visually
impaired Spanish speakers, a less-represented language in this field. In
Spanish, there are few datasets that can be used to create text simplification
systems. Our research has the primary objective to develop a Spanish financial
text simplification dataset. We created a dataset with 5,314 complex and
simplified sentence pairs using established simplification rules. We also
compared our dataset with the simplifications generated from GPT-3, Tuner, and
MT5, in order to evaluate the feasibility of data augmentation using these
systems. In this manuscript we present the characteristics of our dataset and
the findings of the comparisons with other systems. The dataset is available at
Hugging face, saul1917/FEINA. | Artificial Intelligence |
What field is the article from? | Title: Eval-GCSC: A New Metric for Evaluating ChatGPT's Performance in Chinese Spelling Correction
Abstract: ChatGPT has demonstrated impressive performance in various downstream tasks.
However, in the Chinese Spelling Correction (CSC) task, we observe a
discrepancy: while ChatGPT performs well under human evaluation, it scores
poorly according to traditional metrics. We believe this inconsistency arises
because the traditional metrics are not well-suited for evaluating generative
models. Their overly strict length and phonics constraints may lead to
underestimating ChatGPT's correction capabilities. To better evaluate
generative models in the CSC task, this paper proposes a new evaluation metric:
Eval-GCSC. By incorporating word-level and semantic similarity judgments, it
relaxes the stringent length and phonics constraints. Experimental results show
that Eval-GCSC closely aligns with human evaluations. Under this metric,
ChatGPT's performance is comparable to traditional token-level classification
models (TCM), demonstrating its potential as a CSC tool. The source code and
scripts can be accessed at https://github.com/ktlKTL/Eval-GCSC. | Computational Linguistics |
What field is the article from? | Title: Auditing and Mitigating Cultural Bias in LLMs
Abstract: Culture fundamentally shapes people's reasoning, behavior, and communication.
Generative artificial intelligence (AI) technologies may cause a shift towards
a dominant culture. As people increasingly use AI to expedite and even automate
various professional and personal tasks, cultural values embedded in AI models
may bias authentic expression. We audit large language models for cultural
bias, comparing their responses to nationally representative survey data, and
evaluate country-specific prompting as a mitigation strategy. We find that
GPT-4, 3.5 and 3 exhibit cultural values resembling English-speaking and
Protestant European countries. Our mitigation strategy reduces cultural bias in
recent models but not for all countries/territories. To avoid cultural bias in
generative AI, especially in high-stakes contexts, we suggest using culture
matching and ongoing cultural audits. | Computational Linguistics |
What field is the article from? | Title: Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization
Abstract: Histopathological images are essential for medical diagnosis and treatment
planning, but interpreting them accurately using machine learning can be
challenging due to variations in tissue preparation, staining and imaging
protocols. Domain generalization aims to address such limitations by enabling
the learning models to generalize to new datasets or populations. Style
transfer-based data augmentation is an emerging technique that can be used to
improve the generalizability of machine learning models for histopathological
images. However, existing style transfer-based methods can be computationally
expensive, and they rely on artistic styles, which can negatively impact model
accuracy. In this study, we propose a feature domain style mixing technique
that uses adaptive instance normalization to generate style-augmented versions
of images. We compare our proposed method with existing style transfer-based
data augmentation methods and found that it performs similarly or better,
despite requiring less computation and time. Our results demonstrate the
potential of feature domain statistics mixing in the generalization of learning
models for histopathological image analysis. | Computer Vision |
What field is the article from? | Title: OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detection
Abstract: Deep neural networks (DNNs) have been found vulnerable to backdoor attacks,
raising security concerns about their deployment in mission-critical
applications. There are various approaches to detect backdoor attacks, however
they all make certain assumptions about the target attack to be detected and
require equal and huge numbers of clean and backdoor samples for training,
which renders these detection methods quite limiting in real-world
circumstances.
This study proposes a novel one-class classification framework called
One-class Graph Embedding Classification (OCGEC) that uses GNNs for model-level
backdoor detection with only a little amount of clean data. First, we train
thousands of tiny models as raw datasets from a small number of clean datasets.
Following that, we design a ingenious model-to-graph method for converting the
model's structural details and weight features into graph data. We then
pre-train a generative self-supervised graph autoencoder (GAE) to better learn
the features of benign models in order to detect backdoor models without
knowing the attack strategy. After that, we dynamically combine the GAE and
one-class classifier optimization goals to form classification boundaries that
distinguish backdoor models from benign models.
Our OCGEC combines the powerful representation capabilities of graph neural
networks with the utility of one-class classification techniques in the field
of anomaly detection. In comparison to other baselines, it achieves AUC scores
of more than 98% on a number of tasks, which far exceeds existing methods for
detection even when they rely on a huge number of positive and negative
samples. Our pioneering application of graphic scenarios for generic backdoor
detection can provide new insights that can be used to improve other backdoor
defense tasks. Code is available at https://github.com/jhy549/OCGEC. | Machine Learning |
What field is the article from? | Title: RLIF: Interactive Imitation Learning as Reinforcement Learning
Abstract: Although reinforcement learning methods offer a powerful framework for
automatic skill acquisition, for practical learning-based control problems in
domains such as robotics, imitation learning often provides a more convenient
and accessible alternative. In particular, an interactive imitation learning
method such as DAgger, which queries a near-optimal expert to intervene online
to collect correction data for addressing the distributional shift challenges
that afflict na\"ive behavioral cloning, can enjoy good performance both in
theory and practice without requiring manually specified reward functions and
other components of full reinforcement learning methods. In this paper, we
explore how off-policy reinforcement learning can enable improved performance
under assumptions that are similar but potentially even more practical than
those of interactive imitation learning. Our proposed method uses reinforcement
learning with user intervention signals themselves as rewards. This relaxes the
assumption that intervening experts in interactive imitation learning should be
near-optimal and enables the algorithm to learn behaviors that improve over the
potential suboptimal human expert. We also provide a unified framework to
analyze our RL method and DAgger; for which we present the asymptotic analysis
of the suboptimal gap for both methods as well as the non-asymptotic sample
complexity bound of our method. We then evaluate our method on challenging
high-dimensional continuous control simulation benchmarks as well as real-world
robotic vision-based manipulation tasks. The results show that it strongly
outperforms DAgger-like approaches across the different tasks, especially when
the intervening experts are suboptimal. Code and videos can be found on the
project website: rlif-page.github.io | Artificial Intelligence |
What field is the article from? | Title: KEEC: Embed to Control on An Equivariant Geometry
Abstract: This paper investigates how representation learning can enable optimal
control in unknown and complex dynamics, such as chaotic and non-linear
systems, without relying on prior domain knowledge of the dynamics. The core
idea is to establish an equivariant geometry that is diffeomorphic to the
manifold defined by a dynamical system and to perform optimal control within
this corresponding geometry, which is a non-trivial task. To address this
challenge, Koopman Embed to Equivariant Control (KEEC) is proposed for model
learning and control. Inspired by Lie theory, KEEC begins by learning a
non-linear dynamical system defined on a manifold and embedding trajectories
into a Lie group. Subsequently, KEEC formulates an equivariant value function
equation in reinforcement learning on the equivariant geometry, ensuring an
invariant effect as the value function on the original manifold. By deriving
analytical-form optimal actions on the equivariant value function, KEEC
theoretically achieves quadratic convergence for the optimal equivariant value
function by leveraging the differential information on the equivariant
geometry. The effectiveness of KEEC is demonstrated in challenging dynamical
systems, including chaotic ones like Lorenz-63. Notably, our results show that
isometric functions, which maintain the compactness and completeness of
geometry while preserving metric and differential information, consistently
outperform loss functions lacking these characteristics. | Machine Learning |
What field is the article from? | Title: Variants of Tagged Sentential Decision Diagrams
Abstract: A recently proposed canonical form of Boolean functions, namely tagged
sentential decision diagrams (TSDDs), exploits both the standard and
zero-suppressed trimming rules. The standard ones minimize the size of
sentential decision diagrams (SDDs) while the zero-suppressed trimming rules
have the same objective as the standard ones but for zero-suppressed sentential
decision diagrams (ZSDDs). The original TSDDs, which we call zero-suppressed
TSDDs (ZTSDDs), firstly fully utilize the zero-suppressed trimming rules, and
then the standard ones. In this paper, we present a variant of TSDDs which we
call standard TSDDs (STSDDs) by reversing the order of trimming rules. We then
prove the canonicity of STSDDs and present the algorithms for binary operations
on TSDDs. In addition, we offer two kinds of implementations of STSDDs and
ZTSDDs and acquire three variations of the original TSDDs. Experimental
evaluations demonstrate that the four versions of TSDDs have the size advantage
over SDDs and ZSDDs. | Artificial Intelligence |
What field is the article from? | Title: Hyper-Relational Knowledge Graph Neural Network for Next POI
Abstract: With the advancement of mobile technology, Point of Interest (POI)
recommendation systems in Location-based Social Networks (LBSN) have brought
numerous benefits to both users and companies. Many existing works employ
Knowledge Graph (KG) to alleviate the data sparsity issue in LBSN. These
approaches primarily focus on modeling the pair-wise relations in LBSN to
enrich the semantics and thereby relieve the data sparsity issue. However,
existing approaches seldom consider the hyper-relations in LBSN, such as the
mobility relation (a 3-ary relation: user-POI-time). This makes the model hard
to exploit the semantics accurately. In addition, prior works overlook the rich
structural information inherent in KG, which consists of higher-order relations
and can further alleviate the impact of data sparsity.To this end, we propose a
Hyper-Relational Knowledge Graph Neural Network (HKGNN) model. In HKGNN, a
Hyper-Relational Knowledge Graph (HKG) that models the LBSN data is constructed
to maintain and exploit the rich semantics of hyper-relations. Then we proposed
a Hypergraph Neural Network to utilize the structural information of HKG in a
cohesive way. In addition, a self-attention network is used to leverage
sequential information and make personalized recommendations. Furthermore, side
information, essential in reducing data sparsity by providing background
knowledge of POIs, is not fully utilized in current methods. In light of this,
we extended the current dataset with available side information to further
lessen the impact of data sparsity. Results of experiments on four real-world
LBSN datasets demonstrate the effectiveness of our approach compared to
existing state-of-the-art methods. | Artificial Intelligence |
What field is the article from? | Title: Categorizing the Visual Environment and Analyzing the Visual Attention of Dogs
Abstract: Dogs have a unique evolutionary relationship with humans and serve many
important roles e.g. search and rescue, blind assistance, emotional support.
However, few datasets exist to categorize visual features and objects available
to dogs, as well as how dogs direct their visual attention within their
environment. We collect and study a dataset with over 11,698 gazes to
categorize the objects available to be gazed at by 11 dogs in everyday outdoor
environments i.e. a walk around a college campus and urban area. We explore the
availability of these object categories and the visual attention of dogs over
these categories using a head mounted eye tracking apparatus. A small portion
(approx. 600 images or < 20% of total dataset) of the collected data is used to
fine tune a MaskRCNN for the novel image domain to segment objects present in
the scene, enabling further statistical analysis on the visual gaze tendencies
of dogs. The MaskRCNN, with eye tracking apparatus, serves as an end to end
model for automatically classifying the visual fixations of dogs. The fine
tuned MaskRCNN performs far better than chance. There are few individual
differences between the 11 dogs and we observe greater visual fixations on
buses, plants, pavement, and construction equipment. This work takes a step
towards understanding visual behavior of dogs and their interaction with the
physical world. | Computer Vision |
What field is the article from? | Title: ChatGPT-3.5, ChatGPT-4, Google Bard, and Microsoft Bing to Improve Health Literacy and Communication in Pediatric Populations and Beyond
Abstract: Purpose: Enhanced health literacy has been linked to better health outcomes;
however, few interventions have been studied. We investigate whether large
language models (LLMs) can serve as a medium to improve health literacy in
children and other populations.
Methods: We ran 288 conditions using 26 different prompts through
ChatGPT-3.5, Microsoft Bing, and Google Bard. Given constraints imposed by rate
limits, we tested a subset of 150 conditions through ChatGPT-4. The primary
outcome measurements were the reading grade level (RGL) and word counts of
output.
Results: Across all models, output for basic prompts such as "Explain" and
"What is (are)" were at, or exceeded, a 10th-grade RGL. When prompts were
specified to explain conditions from the 1st to 12th RGL, we found that LLMs
had varying abilities to tailor responses based on RGL. ChatGPT-3.5 provided
responses that ranged from the 7th-grade to college freshmen RGL while
ChatGPT-4 outputted responses from the 6th-grade to the college-senior RGL.
Microsoft Bing provided responses from the 9th to 11th RGL while Google Bard
provided responses from the 7th to 10th RGL.
Discussion: ChatGPT-3.5 and ChatGPT-4 did better in achieving lower-grade
level outputs. Meanwhile Bard and Bing tended to consistently produce an RGL
that is at the high school level regardless of prompt. Additionally, Bard's
hesitancy in providing certain outputs indicates a cautious approach towards
health information. LLMs demonstrate promise in enhancing health communication,
but future research should verify the accuracy and effectiveness of such tools
in this context.
Implications: LLMs face challenges in crafting outputs below a sixth-grade
reading level. However, their capability to modify outputs above this threshold
provides a potential mechanism to improve health literacy and communication in
a pediatric population and beyond. | Computational Linguistics |
What field is the article from? | Title: Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education
Abstract: Despite the promises of ML in education, its adoption in the classroom has
surfaced numerous issues regarding fairness, accountability, and transparency,
as well as concerns about data privacy and student consent. A root cause of
these issues is the lack of understanding of the complex dynamics of education,
including teacher-student interactions, collaborative learning, and classroom
environment. To overcome these challenges and fully utilize the potential of ML
in education, software practitioners need to work closely with educators and
students to fully understand the context of the data (the backbone of ML
applications) and collaboratively define the ML data specifications. To gain a
deeper understanding of such a collaborative process, we conduct ten co-design
sessions with ML software practitioners, educators, and students. In the
sessions, teachers and students work with ML engineers, UX designers, and legal
practitioners to define dataset characteristics for a given ML application. We
find that stakeholders contextualize data based on their domain and procedural
knowledge, proactively design data requirements to mitigate downstream harms
and data reliability concerns, and exhibit role-based collaborative strategies
and contribution patterns. Further, we find that beyond a seat at the table,
meaningful stakeholder participation in ML requires structured supports:
defined processes for continuous iteration and co-evaluation, shared contextual
data quality standards, and information scaffolds for both technical and
non-technical stakeholders to traverse expertise boundaries. | Computers and Society |
What field is the article from? | Title: Anatomically-aware Uncertainty for Semi-supervised Image Segmentation
Abstract: Semi-supervised learning relaxes the need of large pixel-wise labeled
datasets for image segmentation by leveraging unlabeled data. A prominent way
to exploit unlabeled data is to regularize model predictions. Since the
predictions of unlabeled data can be unreliable, uncertainty-aware schemes are
typically employed to gradually learn from meaningful and reliable predictions.
Uncertainty estimation methods, however, rely on multiple inferences from the
model predictions that must be computed for each training step, which is
computationally expensive. Moreover, these uncertainty maps capture pixel-wise
disparities and do not consider global information. This work proposes a novel
method to estimate segmentation uncertainty by leveraging global information
from the segmentation masks. More precisely, an anatomically-aware
representation is first learnt to model the available segmentation masks. The
learnt representation thereupon maps the prediction of a new segmentation into
an anatomically-plausible segmentation. The deviation from the plausible
segmentation aids in estimating the underlying pixel-level uncertainty in order
to further guide the segmentation network. The proposed method consequently
estimates the uncertainty using a single inference from our representation,
thereby reducing the total computation. We evaluate our method on two publicly
available segmentation datasets of left atria in cardiac MRIs and of multiple
organs in abdominal CTs. Our anatomically-aware method improves the
segmentation accuracy over the state-of-the-art semi-supervised methods in
terms of two commonly used evaluation metrics. | Computer Vision |
What field is the article from? | Title: StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization
Abstract: In this paper, we investigate the long-term memory learning capabilities of
state-space models (SSMs) from the perspective of parameterization. We prove
that state-space models without any reparameterization exhibit a memory
limitation similar to that of traditional RNNs: the target relationships that
can be stably approximated by state-space models must have an exponential
decaying memory. Our analysis identifies this "curse of memory" as a result of
the recurrent weights converging to a stability boundary, suggesting that a
reparameterization technique can be effective. To this end, we introduce a
class of reparameterization techniques for SSMs that effectively lift its
memory limitations. Besides improving approximation capabilities, we further
illustrate that a principled choice of reparameterization scheme can also
enhance optimization stability. We validate our findings using synthetic
datasets and language models. | Machine Learning |
What field is the article from? | Title: Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroup
Abstract: The deployment of Large Multimodal Models (LMMs) within AntGroup has
significantly advanced multimodal tasks in payment, security, and advertising,
notably enhancing advertisement audition tasks in Alipay. However, the
deployment of such sizable models introduces challenges, particularly in
increased latency and carbon emissions, which are antithetical to the ideals of
Green AI. This paper introduces a novel multi-stage compression strategy for
our proprietary LLM, AntGMM. Our methodology pivots on three main aspects:
employing small training sample sizes, addressing multi-level redundancy
through multi-stage pruning, and introducing an advanced distillation loss
design. In our research, we constructed a dataset, the Multimodal Advertisement
Audition Dataset (MAAD), from real-world scenarios within Alipay, and conducted
experiments to validate the reliability of our proposed strategy. Furthermore,
the effectiveness of our strategy is evident in its operational success in
Alipay's real-world multimodal advertisement audition for three months from
September 2023. Notably, our approach achieved a substantial reduction in
latency, decreasing it from 700ms to 90ms, while maintaining online performance
with only a slight performance decrease. Moreover, our compressed model is
estimated to reduce electricity consumption by approximately 75 million kWh
annually compared to the direct deployment of AntGMM, demonstrating our
commitment to green AI initiatives. We will publicly release our code and the
MAAD dataset after some
reviews\footnote{https://github.com/MorinW/AntGMM$\_$Pruning}. | Artificial Intelligence |
What field is the article from? | Title: Transfer Learning-based Real-time Handgun Detection
Abstract: Traditional surveillance systems rely on human attention, limiting their
effectiveness. This study employs convolutional neural networks and transfer
learning to develop a real-time computer vision system for automatic handgun
detection. Comprehensive analysis of online handgun detection methods is
conducted, emphasizing reducing false positives and learning time. Transfer
learning is demonstrated as an effective approach. Despite technical
challenges, the proposed system achieves a precision rate of 84.74%,
demonstrating promising performance comparable to related works, enabling
faster learning and accurate automatic handgun detection for enhanced security.
This research advances security measures by reducing human monitoring
dependence, showcasing the potential of transfer learning-based approaches for
efficient and reliable handgun detection. | Computer Vision |
What field is the article from? | Title: CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation
Abstract: Medical dialogue generation relies on natural language generation techniques
to enable online medical consultations. Recently, the widespread adoption of
large-scale models in the field of natural language processing has facilitated
rapid advancements in this technology. Existing medical dialogue models are
mostly based on BERT and pre-trained on English corpora, but there is a lack of
high-performing models on the task of Chinese medical dialogue generation. To
solve the above problem, this paper proposes CMed-GPT, which is the GPT
pre-training language model based on Chinese medical domain text. The model is
available in two versions, namely, base and large, with corresponding
perplexity values of 8.64 and 8.01. Additionally, we incorporate lexical and
entity embeddings into the dialogue text in a uniform manner to meet the
requirements of downstream dialogue generation tasks. By applying both
fine-tuning and p-tuning to CMed-GPT, we lowered the PPL from 8.44 to 7.35.
This study not only confirms the exceptional performance of the CMed-GPT model
in generating Chinese biomedical text but also highlights the advantages of
p-tuning over traditional fine-tuning with prefix prompts. Furthermore, we
validate the significance of incorporating external information in medical
dialogue generation, which enhances the quality of dialogue generation. | Computational Linguistics |
What field is the article from? | Title: Accommodating Missing Modalities in Time-Continuous Multimodal Emotion Recognition
Abstract: Decades of research indicate that emotion recognition is more effective when
drawing information from multiple modalities. But what if some modalities are
sometimes missing? To address this problem, we propose a novel
Transformer-based architecture for recognizing valence and arousal in a
time-continuous manner even with missing input modalities. We use a coupling of
cross-attention and self-attention mechanisms to emphasize relationships
between modalities during time and enhance the learning process on weak salient
inputs. Experimental results on the Ulm-TSST dataset show that our model
exhibits an improvement of the concordance correlation coefficient evaluation
of 37% when predicting arousal values and 30% when predicting valence values,
compared to a late-fusion baseline approach. | Machine Learning |
What field is the article from? | Title: Language Models: A Guide for the Perplexed
Abstract: Given the growing importance of AI literacy, we decided to write this
tutorial to help narrow the gap between the discourse among those who study
language models -- the core technology underlying ChatGPT and similar products
-- and those who are intrigued and want to learn more about them. In short, we
believe the perspective of researchers and educators can add some clarity to
the public's understanding of the technologies beyond what's currently
available, which tends to be either extremely technical or promotional material
generated about products by their purveyors.
Our approach teases apart the concept of a language model from products built
on them, from the behaviors attributed to or desired from those products, and
from claims about similarity to human cognition. As a starting point, we (1)
offer a scientific viewpoint that focuses on questions amenable to study
through experimentation; (2) situate language models as they are today in the
context of the research that led to their development; and (3) describe the
boundaries of what is known about the models at this writing. | Computational Linguistics |
What field is the article from? | Title: Maximal Consistent Subsystems of Max-T Fuzzy Relational Equations
Abstract: In this article, we study the inconsistency of a system of $\max-T$ fuzzy
relational equations of the form $A \Box_{T}^{\max} x = b$, where $T$ is a
t-norm among $\min$, the product or Lukasiewicz's t-norm. For an inconsistent
$\max-T$ system, we directly construct a canonical maximal consistent subsystem
(w.r.t the inclusion order). The main tool used to obtain it is the analytical
formula which compute the Chebyshev distance $\Delta = \inf_{c \in \mathcal{C}}
\Vert b - c \Vert$ associated to the inconsistent $\max-T$ system, where
$\mathcal{C}$ is the set of second members of consistent systems defined with
the same matrix $A$. Based on the same analytical formula, we give, for an
inconsistent $\max-\min$ system, an efficient method to obtain all its
consistent subsystems, and we show how to iteratively get all its maximal
consistent subsystems. | Artificial Intelligence |
What field is the article from? | Title: How Far Can Fairness Constraints Help Recover From Biased Data?
Abstract: Blum & Stangl (2019) propose a data bias model to simulate
under-representation and label bias in underprivileged population. For a
stylized data distribution with i.i.d. label noise, under certain simple
conditions on the bias parameters, they show that fair classification with
equal opportunity constraints even on extremely biased distribution can recover
an optimally accurate and fair classifier on the original distribution.
Although their distribution is stylized, their result is interesting because it
demonstrates that fairness constraints can implicitly rectify data bias and
simultaneously overcome a perceived fairness-accuracy trade-off. In this paper,
we give an alternate proof of their result using threshold-based
characterization of optimal fair classifiers. Moreover, we show that their
conditions on the bias parameters are both necessary and sufficient for their
recovery result. Our technique is arguably more flexible, as it readily extends
to more general distributions, e.g., when the labels in the original
distribution have Massart noise instead of i.i.d. noise. Finally, we prove that
for any data distribution, if the optimally accurate classifier in a hypothesis
class is fair and robust, then it can be recovered through fair classification
on the biased distribution, whenever the bias parameters satisfy certain simple
conditions. | Machine Learning |
What field is the article from? | Title: The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding
Abstract: Recent advancements in large vision-language models enabled visual object
detection in open-vocabulary scenarios, where object classes are defined in
free-text formats during inference. In this paper, we aim to probe the
state-of-the-art methods for open-vocabulary object detection to determine to
what extent they understand fine-grained properties of objects and their parts.
To this end, we introduce an evaluation protocol based on dynamic vocabulary
generation to test whether models detect, discern, and assign the correct
fine-grained description to objects in the presence of hard-negative classes.
We contribute with a benchmark suite of increasing difficulty and probing
different properties like color, pattern, and material. We further enhance our
investigation by evaluating several state-of-the-art open-vocabulary object
detectors using the proposed protocol and find that most existing solutions,
which shine in standard open-vocabulary benchmarks, struggle to accurately
capture and distinguish finer object details. We conclude the paper by
highlighting the limitations of current methodologies and exploring promising
research directions to overcome the discovered drawbacks. Data and code are
available at https://github.com/lorebianchi98/FG-OVD. | Computer Vision |
What field is the article from? | Title: Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments
Abstract: This study presents Weighted Sampled Split Learning (WSSL), an innovative
framework tailored to bolster privacy, robustness, and fairness in distributed
machine learning systems. Unlike traditional approaches, WSSL disperses the
learning process among multiple clients, thereby safeguarding data
confidentiality. Central to WSSL's efficacy is its utilization of weighted
sampling. This approach ensures equitable learning by tactically selecting
influential clients based on their contributions. Our evaluation of WSSL
spanned various client configurations and employed two distinct datasets: Human
Gait Sensor and CIFAR-10. We observed three primary benefits: heightened model
accuracy, enhanced robustness, and maintained fairness across diverse client
compositions. Notably, our distributed frameworks consistently surpassed
centralized counterparts, registering accuracy peaks of 82.63% and 75.51% for
the Human Gait Sensor and CIFAR-10 datasets, respectively. These figures
contrast with the top accuracies of 81.12% and 58.60% achieved by centralized
systems. Collectively, our findings champion WSSL as a potent and scalable
successor to conventional centralized learning, marking it as a pivotal stride
forward in privacy-focused, resilient, and impartial distributed machine
learning. | Machine Learning |
What field is the article from? | Title: Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models
Abstract: Deception and persuasion play a critical role in long-horizon dialogues
between multiple parties, especially when the interests, goals, and motivations
of the participants are not aligned. Such complex tasks pose challenges for
current Large Language Models (LLM) as deception and persuasion can easily
mislead them, especially in long-horizon multi-party dialogues. To this end, we
explore the game of Avalon: The Resistance, a social deduction game in which
players must determine each other's hidden identities to complete their team's
objective. We introduce an online testbed and a dataset containing 20 carefully
collected and labeled games among human players that exhibit long-horizon
deception in a cooperative-competitive setting. We discuss the capabilities of
LLMs to utilize deceptive long-horizon conversations between six human players
to determine each player's goal and motivation. Particularly, we discuss the
multimodal integration of the chat between the players and the game's state
that grounds the conversation, providing further insights into the true player
identities. We find that even current state-of-the-art LLMs do not reach human
performance, making our dataset a compelling benchmark to investigate the
decision-making and language-processing capabilities of LLMs. Our dataset and
online testbed can be found at our project website:
https://sstepput.github.io/Avalon-NLU/ | Computational Linguistics |
What field is the article from? | Title: On Training Implicit Meta-Learning With Applications to Inductive Weighing in Consistency Regularization
Abstract: Meta-learning that uses implicit gradient have provided an exciting
alternative to standard techniques which depend on the trajectory of the inner
loop training. Implicit meta-learning (IML), however, require computing
$2^{nd}$ order gradients, particularly the Hessian which is impractical to
compute for modern deep learning models. Various approximations for the Hessian
were proposed but a systematic comparison of their compute cost, stability,
generalization of solution found and estimation accuracy were largely
overlooked. In this study, we start by conducting a systematic comparative
analysis of the various approximation methods and their effect when
incorporated into IML training routines. We establish situations where
catastrophic forgetting is exhibited in IML and explain their cause in terms of
the inability of the approximations to estimate the curvature at convergence
points. Sources of IML training instability are demonstrated and remedied. A
detailed analysis of the effeciency of various inverse Hessian-vector product
approximation methods is also provided. Subsequently, we use the insights
gained to propose and evaluate a novel semi-supervised learning algorithm that
learns to inductively weigh consistency regularization losses. We show how
training a "Confidence Network" to extract domain specific features can learn
to up-weigh useful images and down-weigh out-of-distribution samples. Results
outperform the baseline FixMatch performance. | Machine Learning |
What field is the article from? | Title: AdaptiX -- A Transitional XR Framework for Development and Evaluation of Shared Control Applications in Assistive Robotics
Abstract: With the ongoing efforts to empower people with mobility impairments and the
increase in technological acceptance by the general public, assistive
technologies, such as collaborative robotic arms, are gaining popularity. Yet,
their widespread success is limited by usability issues, specifically the
disparity between user input and software control along the autonomy continuum.
To address this, shared control concepts provide opportunities to combine the
targeted increase of user autonomy with a certain level of computer assistance.
This paper presents the free and open-source AdaptiX XR framework for
developing and evaluating shared control applications in a high-resolution
simulation environment. The initial framework consists of a simulated robotic
arm with an example scenario in Virtual Reality (VR), multiple standard control
interfaces, and a specialized recording/replay system. AdaptiX can easily be
extended for specific research needs, allowing Human-Robot Interaction (HRI)
researchers to rapidly design and test novel interaction methods, intervention
strategies, and multi-modal feedback techniques, without requiring an actual
physical robotic arm during the early phases of ideation, prototyping, and
evaluation. Also, a Robot Operating System (ROS) integration enables the
controlling of a real robotic arm in a PhysicalTwin approach without any
simulation-reality gap. Here, we review the capabilities and limitations of
AdaptiX in detail and present three bodies of research based on the framework.
AdaptiX can be accessed at https://adaptix.robot-research.de. | Human-Computer Interaction |
What field is the article from? | Title: Spreeze: High-Throughput Parallel Reinforcement Learning Framework
Abstract: The promotion of large-scale applications of reinforcement learning (RL)
requires efficient training computation. While existing parallel RL frameworks
encompass a variety of RL algorithms and parallelization techniques, the
excessively burdensome communication frameworks hinder the attainment of the
hardware's limit for final throughput and training effects on a single desktop.
In this paper, we propose Spreeze, a lightweight parallel framework for RL that
efficiently utilizes a single desktop hardware resource to approach the
throughput limit. We asynchronously parallelize the experience sampling,
network update, performance evaluation, and visualization operations, and
employ multiple efficient data transmission techniques to transfer various
types of data between processes. The framework can automatically adjust the
parallelization hyperparameters based on the computing ability of the hardware
device in order to perform efficient large-batch updates. Based on the
characteristics of the "Actor-Critic" RL algorithm, our framework uses dual
GPUs to independently update the network of actors and critics in order to
further improve throughput. Simulation results show that our framework can
achieve up to 15,000Hz experience sampling and 370,000Hz network update frame
rate using only a personal desktop computer, which is an order of magnitude
higher than other mainstream parallel RL frameworks, resulting in a 73%
reduction of training time. Our work on fully utilizing the hardware resources
of a single desktop computer is fundamental to enabling efficient large-scale
distributed RL training. | Machine Learning |
What field is the article from? | Title: Complexity-Guided Curriculum Learning for Text Graphs
Abstract: Curriculum learning provides a systematic approach to training. It refines
training progressively, tailors training to task requirements, and improves
generalization through exposure to diverse examples. We present a curriculum
learning approach that builds on existing knowledge about text and graph
complexity formalisms for training with text graph data. The core part of our
approach is a novel data scheduler, which employs "spaced repetition" and
complexity formalisms to guide the training process. We demonstrate the
effectiveness of the proposed approach on several text graph tasks and graph
neural network architectures. The proposed model gains more and uses less data;
consistently prefers text over graph complexity indices throughout training,
while the best curricula derived from text and graph complexity indices are
equally effective; and it learns transferable curricula across GNN models and
datasets. In addition, we find that both node-level (local) and graph-level
(global) graph complexity indices, as well as shallow and traditional text
complexity indices play a crucial role in effective curriculum learning. | Computational Linguistics |
What field is the article from? | Title: ESG Accountability Made Easy: DocQA at Your Service
Abstract: We present Deep Search DocQA. This application enables information extraction
from documents via a question-answering conversational assistant. The system
integrates several technologies from different AI disciplines consisting of
document conversion to machine-readable format (via computer vision), finding
relevant data (via natural language processing), and formulating an eloquent
response (via large language models). Users can explore over 10,000
Environmental, Social, and Governance (ESG) disclosure reports from over 2000
corporations. The Deep Search platform can be accessed at:
https://ds4sd.github.io. | Computational Linguistics |
What field is the article from? | Title: How ChatGPT is Solving Vulnerability Management Problem
Abstract: Recently, ChatGPT has attracted great attention from the code analysis
domain. Prior works show that ChatGPT has the capabilities of processing
foundational code analysis tasks, such as abstract syntax tree generation,
which indicates the potential of using ChatGPT to comprehend code syntax and
static behaviors. However, it is unclear whether ChatGPT can complete more
complicated real-world vulnerability management tasks, such as the prediction
of security relevance and patch correctness, which require an all-encompassing
understanding of various aspects, including code syntax, program semantics, and
related manual comments.
In this paper, we explore ChatGPT's capabilities on 6 tasks involving the
complete vulnerability management process with a large-scale dataset containing
78,445 samples. For each task, we compare ChatGPT against SOTA approaches,
investigate the impact of different prompts, and explore the difficulties. The
results suggest promising potential in leveraging ChatGPT to assist
vulnerability management. One notable example is ChatGPT's proficiency in tasks
like generating titles for software bug reports. Furthermore, our findings
reveal the difficulties encountered by ChatGPT and shed light on promising
future directions. For instance, directly providing random demonstration
examples in the prompt cannot consistently guarantee good performance in
vulnerability management. By contrast, leveraging ChatGPT in a self-heuristic
way -- extracting expertise from demonstration examples itself and integrating
the extracted expertise in the prompt is a promising research direction.
Besides, ChatGPT may misunderstand and misuse the information in the prompt.
Consequently, effectively guiding ChatGPT to focus on helpful information
rather than the irrelevant content is still an open problem. | Software Engineering |
What field is the article from? | Title: FP8-BERT: Post-Training Quantization for Transformer
Abstract: Transformer-based models, such as BERT, have been widely applied in a wide
range of natural language processing tasks. However, one inevitable side effect
is that they require massive memory storage and inference cost when deployed in
production. Quantization is one of the popularized ways to alleviate the cost.
However, the previous 8-bit quantization strategy based on INT8 data format
either suffers from the degradation of accuracy in a Post-Training Quantization
(PTQ) fashion or requires an expensive Quantization-Aware Training (QAT)
process. Recently, a new numeric format FP8 (i.e. floating-point of 8-bits) has
been proposed and supported in commercial AI computing platforms such as H100.
In this paper, we empirically validate the effectiveness of FP8 as a way to do
Post-Training Quantization without significant loss of accuracy, with a simple
calibration and format conversion process. We adopt the FP8 standard proposed
by NVIDIA Corp. (2022) in our extensive experiments of BERT variants on GLUE
and SQuAD v1.1 datasets, and show that PTQ with FP8 can significantly improve
the accuracy upon that with INT8, to the extent of the full-precision model. | Artificial Intelligence |
What field is the article from? | Title: Examining the Effect of Implementation Factors on Deep Learning Reproducibility
Abstract: Reproducing published deep learning papers to validate their conclusions can
be difficult due to sources of irreproducibility. We investigate the impact
that implementation factors have on the results and how they affect
reproducibility of deep learning studies. Three deep learning experiments were
ran five times each on 13 different hardware environments and four different
software environments. The analysis of the 780 combined results showed that
there was a greater than 6% accuracy range on the same deterministic examples
introduced from hardware or software environment variations alone. To account
for these implementation factors, researchers should run their experiments
multiple times in different hardware and software environments to verify their
conclusions are not affected. | Artificial Intelligence |
What field is the article from? | Title: JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction
Abstract: Product attribute value extraction is an important task in e-Commerce which
can help several downstream applications such as product search and
recommendation. Most previous models handle this task using sequence labeling
or question answering method which rely on the sequential position information
of values in the product text and are vulnerable to data discrepancy between
training and testing. This limits their generalization ability to real-world
scenario in which each product can have multiple descriptions across various
shopping platforms with different composition of text and style. They also have
limited zero-shot ability to new values. In this paper, we propose a multi-task
learning model with value generation/classification and attribute prediction
called JPAVE to predict values without the necessity of position information of
values in the text. Furthermore, the copy mechanism in value generator and the
value attention module in value classifier help our model address the data
discrepancy issue by only focusing on the relevant part of input text and
ignoring other information which causes the discrepancy issue such as sentence
structure in the text. Besides, two variants of our model are designed for
open-world and closed-world scenarios. In addition, copy mechanism introduced
in the first variant based on value generation can improve its zero-shot
ability for identifying unseen values. Experimental results on a public dataset
demonstrate the superiority of our model compared with strong baselines and its
generalization ability of predicting new values. | Computational Linguistics |
What field is the article from? | Title: EDA: Evolving and Distinct Anchors for Multimodal Motion Prediction
Abstract: Motion prediction is a crucial task in autonomous driving, and one of its
major challenges lands in the multimodality of future behaviors. Many
successful works have utilized mixture models which require identification of
positive mixture components, and correspondingly fall into two main lines:
prediction-based and anchor-based matching. The prediction clustering
phenomenon in prediction-based matching makes it difficult to pick
representative trajectories for downstream tasks, while the anchor-based
matching suffers from a limited regression capability. In this paper, we
introduce a novel paradigm, named Evolving and Distinct Anchors (EDA), to
define the positive and negative components for multimodal motion prediction
based on mixture models. We enable anchors to evolve and redistribute
themselves under specific scenes for an enlarged regression capacity.
Furthermore, we select distinct anchors before matching them with the ground
truth, which results in impressive scoring performance. Our approach enhances
all metrics compared to the baseline MTR, particularly with a notable relative
reduction of 13.5% in Miss Rate, resulting in state-of-the-art performance on
the Waymo Open Motion Dataset. Code is available at
https://github.com/Longzhong-Lin/EDA. | Computer Vision |
What field is the article from? | Title: Modular Blended Attention Network for Video Question Answering
Abstract: In multimodal machine learning tasks, it is due to the complexity of the
assignments that the network structure, in most cases, is assembled in a
sophisticated way. The holistic architecture can be separated into several
logical parts according to the respective ends that the modules are devised to
achieve. As the number of modalities of information representation increases,
constructing ad hoc subnetworks for processing the data from divergent
modalities while mediating the fusion of different information types has become
a cumbersome and expensive problem. In this paper, we present an approach to
facilitate the question with a reusable and composable neural unit; by
connecting the units in series or parallel, the arduous network constructing of
multimodal machine learning tasks will be accomplished in a much
straightforward way. Additionally, through parameter sharing (weights
replication) among the units, the space complexity will be significantly
reduced. We have conducted experiments on three commonly used datasets; our
method achieves impressive performance compared to several video QA baselines. | Computer Vision |
What field is the article from? | Title: Investigating Deep-Learning NLP for Automating the Extraction of Oncology Efficacy Endpoints from Scientific Literature
Abstract: Benchmarking drug efficacy is a critical step in clinical trial design and
planning. The challenge is that much of the data on efficacy endpoints is
stored in scientific papers in free text form, so extraction of such data is
currently a largely manual task. Our objective is to automate this task as much
as possible. In this study we have developed and optimised a framework to
extract efficacy endpoints from text in scientific papers, using a machine
learning approach. Our machine learning model predicts 25 classes associated
with efficacy endpoints and leads to high F1 scores (harmonic mean of precision
and recall) of 96.4% on the test set, and 93.9% and 93.7% on two case studies.
These methods were evaluated against - and showed strong agreement with -
subject matter experts and show significant promise in the future of automating
the extraction of clinical endpoints from free text. Clinical information
extraction from text data is currently a laborious manual task which scales
poorly and is prone to human error. Demonstrating the ability to extract
efficacy endpoints automatically shows great promise for accelerating clinical
trial design moving forwards. | Computational Linguistics |
What field is the article from? | Title: Unveiling Empirical Pathologies of Laplace Approximation for Uncertainty Estimation
Abstract: In this paper, we critically evaluate Bayesian methods for uncertainty
estimation in deep learning, focusing on the widely applied Laplace
approximation and its variants. Our findings reveal that the conventional
method of fitting the Hessian matrix negatively impacts out-of-distribution
(OOD) detection efficiency. We propose a different point of view, asserting
that focusing solely on optimizing prior precision can yield more accurate
uncertainty estimates in OOD detection while preserving adequate calibration
metrics. Moreover, we demonstrate that this property is not connected to the
training stage of a model but rather to its intrinsic properties. Through
extensive experimental evaluation, we establish the superiority of our
simplified approach over traditional methods in the out-of-distribution domain. | Machine Learning |
What field is the article from? | Title: One Shot Learning as Instruction Data Prospector for Large Language Models
Abstract: Aligning large language models(LLMs) with human is a critical step in
effectively utilizing their pre-trained capabilities across a wide array of
language tasks. Current instruction tuning practices often rely on expanding
dataset size without a clear strategy for ensuring data quality, which can
inadvertently introduce noise and degrade model performance. To address this
challenge, we introduce Nuggets, a novel and efficient methodology that employs
one shot learning to select high-quality instruction data from expansive
datasets. Nuggets assesses the potential of individual instruction examples to
act as effective one shot examples, thereby identifying those that can
significantly enhance diverse task performance. Nuggets utilizes a scoring
system based on the impact of candidate examples on the perplexity of a diverse
anchor set, facilitating the selection of the most beneficial data for
instruction tuning. Through rigorous testing on two benchmarks, including
MT-Bench and Alpaca-Eval, we demonstrate that instruction tuning with the top
1% of Nuggets-curated examples substantially outperforms conventional methods
that use the full dataset. These findings advocate for a data selection
paradigm that prioritizes quality, offering a more efficient pathway to align
LLMs with humans. | Computational Linguistics |
What field is the article from? | Title: Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs
Abstract: We propose a novel multi-bit box-free watermarking method for the protection
of Intellectual Property Rights (IPR) of GANs with improved robustness against
white-box attacks like fine-tuning, pruning, quantization, and surrogate model
attacks. The watermark is embedded by adding an extra watermarking loss term
during GAN training, ensuring that the images generated by the GAN contain an
invisible watermark that can be retrieved by a pre-trained watermark decoder.
In order to improve the robustness against white-box model-level attacks, we
make sure that the model converges to a wide flat minimum of the watermarking
loss term, in such a way that any modification of the model parameters does not
erase the watermark. To do so, we add random noise vectors to the parameters of
the generator and require that the watermarking loss term is as invariant as
possible with respect to the presence of noise. This procedure forces the
generator to converge to a wide flat minimum of the watermarking loss. The
proposed method is architectureand dataset-agnostic, thus being applicable to
many different generation tasks and models, as well as to CNN-based image
processing architectures. We present the results of extensive experiments
showing that the presence of the watermark has a negligible impact on the
quality of the generated images, and proving the superior robustness of the
watermark against model modification and surrogate model attacks. | Computer Vision |
What field is the article from? | Title: Bayesian Neural Networks: A Min-Max Game Framework
Abstract: Bayesian neural networks use random variables to describe the neural networks
rather than deterministic neural networks and are mostly trained by variational
inference which updates the mean and variance at the same time. Here, we
formulate the Bayesian neural networks as a minimax game problem. We do the
experiments on the MNIST data set and the primary result is comparable to the
existing closed-loop transcription neural network. Finally, we reveal the
connections between Bayesian neural networks and closed-loop transcription
neural networks, and show our framework is rather practical, and provide
another view of Bayesian neural networks. | Machine Learning |
What field is the article from? | Title: A Weighted K-Center Algorithm for Data Subset Selection
Abstract: The success of deep learning hinges on enormous data and large models, which
require labor-intensive annotations and heavy computation costs. Subset
selection is a fundamental problem that can play a key role in identifying
smaller portions of the training data, which can then be used to produce
similar models as the ones trained with full data. Two prior methods are shown
to achieve impressive results: (1) margin sampling that focuses on selecting
points with high uncertainty, and (2) core-sets or clustering methods such as
k-center for informative and diverse subsets. We are not aware of any work that
combines these methods in a principled manner. To this end, we develop a novel
and efficient factor 3-approximation algorithm to compute subsets based on the
weighted sum of both k-center and uncertainty sampling objective functions. To
handle large datasets, we show a parallel algorithm to run on multiple machines
with approximation guarantees. The proposed algorithm achieves similar or
better performance compared to other strong baselines on vision datasets such
as CIFAR-10, CIFAR-100, and ImageNet. | Machine Learning |
What field is the article from? | Title: Mesh Neural Cellular Automata
Abstract: Modeling and synthesizing textures are essential for enhancing the realism of
virtual environments. Methods that directly synthesize textures in 3D offer
distinct advantages to the UV-mapping-based methods as they can create seamless
textures and align more closely with the ways textures form in nature. We
propose Mesh Neural Cellular Automata (MeshNCA), a method for directly
synthesizing dynamic textures on 3D meshes without requiring any UV maps.
MeshNCA is a generalized type of cellular automata that can operate on a set of
cells arranged on a non-grid structure such as vertices of a 3D mesh. While
only being trained on an Icosphere mesh, MeshNCA shows remarkable
generalization and can synthesize textures on any mesh in real time after the
training. Additionally, it accommodates multi-modal supervision and can be
trained using different targets such as images, text prompts, and motion vector
fields. Moreover, we conceptualize a way of grafting trained MeshNCA instances,
enabling texture interpolation. Our MeshNCA model enables real-time 3D texture
synthesis on meshes and allows several user interactions including texture
density/orientation control, a grafting brush, and motion speed/direction
control. Finally, we implement the forward pass of our MeshNCA model using the
WebGL shading language and showcase our trained models in an online interactive
demo which is accessible on personal computers and smartphones. Our demo and
the high resolution version of this PDF are available at
https://meshnca.github.io/. | Computer Vision |
What field is the article from? | Title: ArTST: Arabic Text and Speech Transformer
Abstract: We present ArTST, a pre-trained Arabic text and speech transformer for
supporting open-source speech technologies for the Arabic language. The model
architecture follows the unified-modal framework, SpeechT5, that was recently
released for English, and is focused on Modern Standard Arabic (MSA), with
plans to extend the model for dialectal and code-switched Arabic in future
editions. We pre-trained the model from scratch on MSA speech and text data,
and fine-tuned it for the following tasks: Automatic Speech Recognition (ASR),
Text-To-Speech synthesis (TTS), and spoken dialect identification. In our
experiments comparing ArTST with SpeechT5, as well as with previously reported
results in these tasks, ArTST performs on a par with or exceeding the current
state-of-the-art in all three tasks. Moreover, we find that our pre-training is
conducive for generalization, which is particularly evident in the low-resource
TTS task. The pre-trained model as well as the fine-tuned ASR and TTS models
are released for research use. | Computational Linguistics |
What field is the article from? | Title: TimelyGPT: Recurrent Convolutional Transformer for Long Time-series Representation
Abstract: Pre-trained models (PTMs) have gained prominence in Natural Language
Processing and Computer Vision domains. When it comes to time-series PTMs,
their development has been limited. Previous research on time-series
transformers has mainly been devoted to small-scale tasks, yet these models
have not consistently outperformed traditional models. Additionally, the
performance of these transformers on large-scale data remains unexplored. These
findings raise doubts about Transformer's capabilities to scale up and capture
temporal dependencies. In this study, we re-examine time-series transformers
and identify the shortcomings of prior studies. Drawing from these insights, we
then introduce a pioneering architecture called Timely Generative Pre-trained
Transformer (\model). This architecture integrates recurrent attention and
temporal convolution modules to effectively capture global-local temporal
dependencies in long sequences. The relative position embedding with time decay
can effectively deal with trend and periodic patterns from time-series. Our
experiments show that \model~excels in modeling continuously monitored
biosignal as well as irregularly-sampled time-series data commonly observed in
longitudinal electronic health records. This breakthrough suggests a priority
shift in time-series deep learning research, moving from small-scale modeling
from scratch to large-scale pre-training. | Machine Learning |
What field is the article from? | Title: Scheming AIs: Will AIs fake alignment during training in order to get power?
Abstract: This report examines whether advanced AIs that perform well in training will
be doing so in order to gain power later -- a behavior I call "scheming" (also
sometimes called "deceptive alignment"). I conclude that scheming is a
disturbingly plausible outcome of using baseline machine learning methods to
train goal-directed AIs sophisticated enough to scheme (my subjective
probability on such an outcome, given these conditions, is roughly 25%). In
particular: if performing well in training is a good strategy for gaining power
(as I think it might well be), then a very wide variety of goals would motivate
scheming -- and hence, good training performance. This makes it plausible that
training might either land on such a goal naturally and then reinforce it, or
actively push a model's motivations towards such a goal as an easy way of
improving performance. What's more, because schemers pretend to be aligned on
tests designed to reveal their motivations, it may be quite difficult to tell
whether this has occurred. However, I also think there are reasons for comfort.
In particular: scheming may not actually be such a good strategy for gaining
power; various selection pressures in training might work against schemer-like
goals (for example, relative to non-schemers, schemers need to engage in extra
instrumental reasoning, which might harm their training performance); and we
may be able to increase such pressures intentionally. The report discusses
these and a wide variety of other considerations in detail, and it suggests an
array of empirical research directions for probing the topic further. | Computers and Society |
What field is the article from? | Title: A Universal Anti-Spoofing Approach for Contactless Fingerprint Biometric Systems
Abstract: With the increasing integration of smartphones into our daily lives,
fingerphotos are becoming a potential contactless authentication method. While
it offers convenience, it is also more vulnerable to spoofing using various
presentation attack instruments (PAI). The contactless fingerprint is an
emerging biometric authentication but has not yet been heavily investigated for
anti-spoofing. While existing anti-spoofing approaches demonstrated fair
results, they have encountered challenges in terms of universality and
scalability to detect any unseen/unknown spoofed samples. To address this
issue, we propose a universal presentation attack detection method for
contactless fingerprints, despite having limited knowledge of presentation
attack samples. We generated synthetic contactless fingerprints using StyleGAN
from live finger photos and integrating them to train a semi-supervised
ResNet-18 model. A novel joint loss function, combining the Arcface and Center
loss, is introduced with a regularization to balance between the two loss
functions and minimize the variations within the live samples while enhancing
the inter-class variations between the deepfake and live samples. We also
conducted a comprehensive comparison of different regularizations' impact on
the joint loss function for presentation attack detection (PAD) and explored
the performance of a modified ResNet-18 architecture with different activation
functions (i.e., leaky ReLU and RelU) in conjunction with Arcface and center
loss. Finally, we evaluate the performance of the model using unseen types of
spoof attacks and live data. Our proposed method achieves a Bona Fide
Classification Error Rate (BPCER) of 0.12\%, an Attack Presentation
Classification Error Rate (APCER) of 0.63\%, and an Average Classification
Error Rate (ACER) of 0.37\%. | Computer Vision |
What field is the article from? | Title: Grounding for Artificial Intelligence
Abstract: A core function of intelligence is grounding, which is the process of
connecting the natural language and abstract knowledge to the internal
representation of the real world in an intelligent being, e.g., a human. Human
cognition is grounded in our sensorimotor experiences in the external world and
subjective feelings in our internal world. We use languages to communicate with
each other and the languages are grounded on our shared sensorimotor
experiences and feelings. Without this shard grounding, it is impossible for us
to understand each other because all natural languages are highly abstract and
are only able to describe a tiny portion of what has happened or is happening
in the real world. Although grounding at high or abstract levels has been
studied in different fields and applications, to our knowledge, limited
systematic work at fine-grained levels has been done. With the rapid progress
of large language models (LLMs), it is imperative that we have a sound
understanding of grounding in order to move to the next level of intelligence.
It is also believed that grounding is necessary for Artificial General
Intelligence (AGI). This paper makes an attempt to systematically study this
problem. | Artificial Intelligence |
What field is the article from? | Title: Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery
Abstract: Deep reinforcement learning suffers from catastrophic forgetting and sample
inefficiency making it less applicable to the ever-changing real world.
However, the ability to use previously learned knowledge is essential for AI
agents to quickly adapt to novelties. Often, certain spatial information
observed by the agent in the previous interactions can be leveraged to infer
task-specific rules. Inferred rules can then help the agent to avoid
potentially dangerous situations in the previously unseen states and guide the
learning process increasing agent's novelty adaptation speed. In this work, we
propose a general framework that is applicable to deep reinforcement learning
agents. Our framework provides the agent with an autonomous way to discover the
task-specific rules in the novel environments and self-supervise it's learning.
We provide a rule-driven deep Q-learning agent (RDQ) as one possible
implementation of that framework. We show that RDQ successfully extracts
task-specific rules as it interacts with the world and uses them to drastically
increase its learning efficiency. In our experiments, we show that the RDQ
agent is significantly more resilient to the novelties than the baseline
agents, and is able to detect and adapt to novel situations faster. | Artificial Intelligence |
What field is the article from? | Title: Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things
Abstract: The Internet of Medical Things (IoMT) has dramatically benefited medical
professionals that patients and physicians can access from all regions.
Although the automatic detection and prediction of diseases such as melanoma
and leukemia is still being researched and studied in IoMT, existing approaches
are not able to achieve a high degree of efficiency. Thus, with a new approach
that provides better results, patients would access the adequate treatments
earlier and the death rate would be reduced. Therefore, this paper introduces
an IoMT proposal for medical images classification that may be used anywhere,
i.e. it is an ubiquitous approach. It was design in two stages: first, we
employ a Transfer Learning (TL)-based method for feature extraction, which is
carried out using MobileNetV3; second, we use the Chaos Game Optimization (CGO)
for feature selection, with the aim of excluding unnecessary features and
improving the performance, which is key in IoMT. Our methodology was evaluated
using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results
indicated that the proposed approach obtained an accuracy of 88.39% on
ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell. Moreover, our approach had
successful performances for the metrics employed compared to other existing
methods. | Computer Vision |
What field is the article from? | Title: An Expectation-Realization Model for Metaphor Detection
Abstract: We propose a metaphor detection architecture that is structured around two
main modules: an expectation component that estimates representations of
literal word expectations given a context, and a realization component that
computes representations of actual word meanings in context. The overall
architecture is trained to learn expectation-realization (ER) patterns that
characterize metaphorical uses of words. When evaluated on three metaphor
datasets for within distribution, out of distribution, and novel metaphor
generalization, the proposed method is shown to obtain results that are
competitive or better than state-of-the art. Further increases in metaphor
detection accuracy are obtained through ensembling of ER models. | Computational Linguistics |
What field is the article from? | Title: Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection
Abstract: Real-world social events typically exhibit a severe class-imbalance
distribution, which makes the trained detection model encounter a serious
generalization challenge. Most studies solve this problem from the frequency
perspective and emphasize the representation or classifier learning for tail
classes. While in our observation, compared to the rarity of classes, the
calibrated uncertainty estimated from well-trained evidential deep learning
networks better reflects model performance. To this end, we propose a novel
uncertainty-guided class imbalance learning framework - UCL$_{SED}$, and its
variant - UCL-EC$_{SED}$, for imbalanced social event detection tasks. We aim
to improve the overall model performance by enhancing model generalization to
those uncertain classes. Considering performance degradation usually comes from
misclassifying samples as their confusing neighboring classes, we focus on
boundary learning in latent space and classifier learning with high-quality
uncertainty estimation. First, we design a novel uncertainty-guided contrastive
learning loss, namely UCL and its variant - UCL-EC, to manipulate
distinguishable representation distribution for imbalanced data. During
training, they force all classes, especially uncertain ones, to adaptively
adjust a clear separable boundary in the feature space. Second, to obtain more
robust and accurate class uncertainty, we combine the results of multi-view
evidential classifiers via the Dempster-Shafer theory under the supervision of
an additional calibration method. We conduct experiments on three severely
imbalanced social event datasets including Events2012\_100, Events2018\_100,
and CrisisLexT\_7. Our model significantly improves social event representation
and classification tasks in almost all classes, especially those uncertain
ones. | Artificial Intelligence |
What field is the article from? | Title: KirchhoffNet: A Circuit Bridging Message Passing and Continuous-Depth Models
Abstract: In this paper, we exploit a fundamental principle of analog electronic
circuitry, Kirchhoff's current law, to introduce a unique class of neural
network models that we refer to as KirchhoffNet. KirchhoffNet establishes close
connections with message passing neural networks and continuous-depth networks.
We demonstrate that even in the absence of any traditional layers (such as
convolution, pooling, or linear layers), KirchhoffNet attains 98.86% test
accuracy on the MNIST dataset, comparable with state of the art (SOTA) results.
What makes KirchhoffNet more intriguing is its potential in the realm of
hardware. Contemporary deep neural networks are conventionally deployed on
GPUs. In contrast, KirchhoffNet can be physically realized by an analog
electronic circuit. Moreover, we justify that irrespective of the number of
parameters within a KirchhoffNet, its forward calculation can always be
completed within 1/f seconds, with f representing the hardware's clock
frequency. This characteristic introduces a promising technology for
implementing ultra-large-scale neural networks. | Machine Learning |
What field is the article from? | Title: Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
Abstract: Learning graphical conditional independence structures is an important
machine learning problem and a cornerstone of causal discovery. However, the
accuracy and execution time of learning algorithms generally struggle to scale
to problems with hundreds of highly connected variables -- for instance,
recovering brain networks from fMRI data. We introduce the best order score
search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs
(DAGs) in this paradigm. BOSS greedily searches over permutations of variables,
using GSTs to construct and score DAGs from permutations. GSTs efficiently
cache scores to eliminate redundant calculations. BOSS achieves
state-of-the-art performance in accuracy and execution time, comparing
favorably to a variety of combinatorial and gradient-based learning algorithms
under a broad range of conditions. To demonstrate its practicality, we apply
BOSS to two sets of resting-state fMRI data: simulated data with
pseudo-empirical noise distributions derived from randomized empirical fMRI
cortical signals and clinical data from 3T fMRI scans processed into cortical
parcels. BOSS is available for use within the TETRAD project which includes
Python and R wrappers. | Machine Learning |
What field is the article from? | Title: Muscle volume quantification: guiding transformers with anatomical priors
Abstract: Muscle volume is a useful quantitative biomarker in sports, but also for the
follow-up of degenerative musculo-skelletal diseases. In addition to volume,
other shape biomarkers can be extracted by segmenting the muscles of interest
from medical images. Manual segmentation is still today the gold standard for
such measurements despite being very time-consuming. We propose a method for
automatic segmentation of 18 muscles of the lower limb on 3D Magnetic Resonance
Images to assist such morphometric analysis. By their nature, the tissue of
different muscles is undistinguishable when observed in MR Images. Thus, muscle
segmentation algorithms cannot rely on appearance but only on contour cues.
However, such contours are hard to detect and their thickness varies across
subjects. To cope with the above challenges, we propose a segmentation approach
based on a hybrid architecture, combining convolutional and visual transformer
blocks. We investigate for the first time the behaviour of such hybrid
architectures in the context of muscle segmentation for shape analysis.
Considering the consistent anatomical muscle configuration, we rely on
transformer blocks to capture the longrange relations between the muscles. To
further exploit the anatomical priors, a second contribution of this work
consists in adding a regularisation loss based on an adjacency matrix of
plausible muscle neighbourhoods estimated from the training data. Our
experimental results on a unique database of elite athletes show it is possible
to train complex hybrid models from a relatively small database of large
volumes, while the anatomical prior regularisation favours better predictions. | Computer Vision |
What field is the article from? | Title: Gene-MOE: A sparsely gated prognosis and classification framework exploiting pan-cancer genomic information
Abstract: Benefiting from the advancements in deep learning, various genomic analytical
techniques, such as survival analysis, classification of tumors and their
subtypes, and exploration of specific pathways, have significantly enhanced our
understanding of the biological mechanisms driving cancer. However, the
overfitting issue, arising from the limited number of patient samples, poses a
challenge in improving the accuracy of genome analysis by deepening the neural
network. Furthermore, it remains uncertain whether novel approaches such as the
sparsely gated mixture of expert (MOE) and self-attention mechanisms can
improve the accuracy of genomic analysis. In this paper, we introduce a novel
sparsely gated RNA-seq analysis framework called Gene-MOE. This framework
exploits the potential of the MOE layers and the proposed mixture of attention
expert (MOAE) layers to enhance the analysis accuracy. Additionally, it
addresses overfitting challenges by integrating pan-cancer information from 33
distinct cancer types through pre-training.We pre-trained Gene-MOE on TCGA
pan-cancer RNA-seq dataset with 33 cancer types. Subsequently, we conducted
experiments involving cancer classification and survival analysis based on the
pre-trained Gene-MOE. According to the survival analysis results on 14 cancer
types, Gene-MOE outperformed state-of-the-art models on 12 cancer types.
Through detailed feature analysis, we found that the Gene-MOE model could learn
rich feature representations of high-dimensional genes. According to the
classification results, the total accuracy of the classification model for 33
cancer classifications reached 95.8%, representing the best performance
compared to state-of-the-art models. These results indicate that Gene-MOE holds
strong potential for use in cancer classification and survival analysis. | Machine Learning |
What field is the article from? | Title: WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models
Abstract: To mitigate the potential misuse of large language models (LLMs), recent
research has developed watermarking algorithms, which restrict the generation
process to leave an invisible trace for watermark detection. Due to the
two-stage nature of the task, most studies evaluate the generation and
detection separately, thereby presenting a challenge in unbiased, thorough, and
applicable evaluations. In this paper, we introduce WaterBench, the first
comprehensive benchmark for LLM watermarks, in which we design three crucial
factors: (1) For \textbf{benchmarking procedure}, to ensure an apples-to-apples
comparison, we first adjust each watermarking method's hyper-parameter to reach
the same watermarking strength, then jointly evaluate their generation and
detection performance. (2) For \textbf{task selection}, we diversify the input
and output length to form a five-category taxonomy, covering $9$ tasks. (3) For
\textbf{evaluation metric}, we adopt the GPT4-Judge for automatically
evaluating the decline of instruction-following abilities after watermarking.
We evaluate $4$ open-source watermarks on $2$ LLMs under $2$ watermarking
strengths and observe the common struggles for current methods on maintaining
the generation quality. The code and data are available at
\url{https://github.com/THU-KEG/WaterBench}. | Computational Linguistics |
What field is the article from? | Title: CoSeR: Bridging Image and Language for Cognitive Super-Resolution
Abstract: Existing super-resolution (SR) models primarily focus on restoring local
texture details, often neglecting the global semantic information within the
scene. This oversight can lead to the omission of crucial semantic details or
the introduction of inaccurate textures during the recovery process. In our
work, we introduce the Cognitive Super-Resolution (CoSeR) framework, empowering
SR models with the capacity to comprehend low-resolution images. We achieve
this by marrying image appearance and language understanding to generate a
cognitive embedding, which not only activates prior information from large
text-to-image diffusion models but also facilitates the generation of
high-quality reference images to optimize the SR process. To further improve
image fidelity, we propose a novel condition injection scheme called
"All-in-Attention", consolidating all conditional information into a single
module. Consequently, our method successfully restores semantically correct and
photorealistic details, demonstrating state-of-the-art performance across
multiple benchmarks. Code: https://github.com/VINHYU/CoSeR | Computer Vision |
What field is the article from? | Title: Mixture of Weak & Strong Experts on Graphs
Abstract: Realistic graphs contain both rich self-features of nodes and informative
structures of neighborhoods, jointly handled by a GNN in the typical setup. We
propose to decouple the two modalities by mixture of weak and strong experts
(Mowst), where the weak expert is a light-weight Multi-layer Perceptron (MLP),
and the strong expert is an off-the-shelf Graph Neural Network (GNN). To adapt
the experts' collaboration to different target nodes, we propose a "confidence"
mechanism based on the dispersion of the weak expert's prediction logits. The
strong expert is conditionally activated when either the node's classification
relies on neighborhood information, or the weak expert has low model quality.
We reveal interesting training dynamics by analyzing the influence of the
confidence function on loss: our training algorithm encourages the
specialization of each expert by effectively generating soft splitting of the
graph. In addition, our "confidence" design imposes a desirable bias toward the
strong expert to benefit from GNN's better generalization capability. Mowst is
easy to optimize and achieves strong expressive power, with a computation cost
comparable to a single GNN. Empirically, Mowst shows significant accuracy
improvement on 6 standard node classification benchmarks (including both
homophilous and heterophilous graphs). | Machine Learning |
What field is the article from? | Title: Co-training and Co-distillation for Quality Improvement and Compression of Language Models
Abstract: Knowledge Distillation (KD) compresses computationally expensive pre-trained
language models (PLMs) by transferring their knowledge to smaller models,
allowing their use in resource-constrained or real-time settings. However, most
smaller models fail to surpass the performance of the original larger model,
resulting in sacrificing performance to improve inference speed. To address
this issue, we propose Co-Training and Co-Distillation (CTCD), a novel
framework that improves performance and inference speed together by co-training
two models while mutually distilling knowledge. The CTCD framework successfully
achieves this based on two significant findings: 1) Distilling knowledge from
the smaller model to the larger model during co-training improves the
performance of the larger model. 2) The enhanced performance of the larger
model further boosts the performance of the smaller model. The CTCD framework
shows promise as it can be combined with existing techniques like architecture
design or data augmentation, replacing one-way KD methods, to achieve further
performance improvement. Extensive ablation studies demonstrate the
effectiveness of CTCD, and the small model distilled by CTCD outperforms the
original larger model by a significant margin of 1.66 on the GLUE benchmark. | Computational Linguistics |
What field is the article from? | Title: Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models
Abstract: Large Language Models (LLMs) have generated considerable interest and debate
regarding their potential emergence of Theory of Mind (ToM). Several recent
inquiries reveal a lack of robust ToM in these models and pose a pressing
demand to develop new benchmarks, as current ones primarily focus on different
aspects of ToM and are prone to shortcuts and data leakage. In this position
paper, we seek to answer two road-blocking questions: (1) How can we taxonomize
a holistic landscape of machine ToM? (2) What is a more effective evaluation
protocol for machine ToM? Following psychological studies, we taxonomize
machine ToM into 7 mental state categories and delineate existing benchmarks to
identify under-explored aspects of ToM. We argue for a holistic and situated
evaluation of ToM to break ToM into individual components and treat LLMs as an
agent who is physically situated in environments and socially situated in
interactions with humans. Such situated evaluation provides a more
comprehensive assessment of mental states and potentially mitigates the risk of
shortcuts and data leakage. We further present a pilot study in a grid world
setup as a proof of concept. We hope this position paper can facilitate future
research to integrate ToM with LLMs and offer an intuitive means for
researchers to better position their work in the landscape of ToM. Project
page: https://github.com/Mars-tin/awesome-theory-of-mind | Computational Linguistics |
What field is the article from? | Title: LanGWM: Language Grounded World Model
Abstract: Recent advances in deep reinforcement learning have showcased its potential
in tackling complex tasks. However, experiments on visual control tasks have
revealed that state-of-the-art reinforcement learning models struggle with
out-of-distribution generalization. Conversely, expressing higher-level
concepts and global contexts is relatively easy using language.
Building upon recent success of the large language models, our main objective
is to improve the state abstraction technique in reinforcement learning by
leveraging language for robust action selection. Specifically, we focus on
learning language-grounded visual features to enhance the world model learning,
a model-based reinforcement learning technique.
To enforce our hypothesis explicitly, we mask out the bounding boxes of a few
objects in the image observation and provide the text prompt as descriptions
for these masked objects. Subsequently, we predict the masked objects along
with the surrounding regions as pixel reconstruction, similar to the
transformer-based masked autoencoder approach.
Our proposed LanGWM: Language Grounded World Model achieves state-of-the-art
performance in out-of-distribution test at the 100K interaction steps
benchmarks of iGibson point navigation tasks. Furthermore, our proposed
technique of explicit language-grounded visual representation learning has the
potential to improve models for human-robot interaction because our extracted
visual features are language grounded. | Machine Learning |
What field is the article from? | Title: From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach
Abstract: We propose the Kuramoto Graph Neural Network (KuramotoGNN), a novel class of
continuous-depth graph neural networks (GNNs) that employs the Kuramoto model
to mitigate the over-smoothing phenomenon, in which node features in GNNs
become indistinguishable as the number of layers increases. The Kuramoto model
captures the synchronization behavior of non-linear coupled oscillators. Under
the view of coupled oscillators, we first show the connection between Kuramoto
model and basic GNN and then over-smoothing phenomenon in GNNs can be
interpreted as phase synchronization in Kuramoto model. The KuramotoGNN
replaces this phase synchronization with frequency synchronization to prevent
the node features from converging into each other while allowing the system to
reach a stable synchronized state. We experimentally verify the advantages of
the KuramotoGNN over the baseline GNNs and existing methods in reducing
over-smoothing on various graph deep learning benchmark tasks. | Machine Learning |
What field is the article from? | Title: MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications
Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across
various natural language tasks, marking significant strides towards general
artificial intelligence. While general artificial intelligence is leveraged by
developing increasingly large-scale models, there could be another branch to
develop lightweight custom models that better serve certain domains, taking
into account the high cost of training and deploying LLMs and the scarcity of
resources. In this paper, we present MindLLM, a novel series of bilingual
lightweight large language models, trained from scratch, alleviating such
burdens by offering models with 1.3 billion and 3 billion parameters. A
thorough account of experiences accrued during large model development is
given, covering every step of the process, including data construction, model
architecture, evaluation, and applications. Such insights are hopefully
valuable for fellow academics and developers. MindLLM consistently matches or
surpasses the performance of other open-source larger models on some public
benchmarks. We also introduce an innovative instruction tuning framework
tailored for smaller models to enhance their capabilities efficiently.
Moreover, we explore the application of MindLLM in specific vertical domains
such as law and finance, underscoring the agility and adaptability of our
lightweight models. | Computational Linguistics |
What field is the article from? | Title: CERN for AGI: A Theoretical Framework for Autonomous Simulation-Based Artificial Intelligence Testing and Alignment
Abstract: This paper explores the potential of a multidisciplinary approach to testing
and aligning artificial general intelligence (AGI) and LLMs. Due to the rapid
development and wide application of LLMs, challenges such as ethical alignment,
controllability, and predictability of these models have become important
research topics. This study investigates an innovative simulation-based
multi-agent system within a virtual reality framework that replicates the
real-world environment. The framework is populated by automated 'digital
citizens,' simulating complex social structures and interactions to examine and
optimize AGI. Application of various theories from the fields of sociology,
social psychology, computer science, physics, biology, and economics
demonstrates the possibility of a more human-aligned and socially responsible
AGI. The purpose of such a digital environment is to provide a dynamic platform
where advanced AI agents can interact and make independent decisions, thereby
mimicking realistic scenarios. The actors in this digital city, operated by the
LLMs, serve as the primary agents, exhibiting high degrees of autonomy. While
this approach shows immense potential, there are notable challenges and
limitations, most significantly the unpredictable nature of real-world social
dynamics. This research endeavors to contribute to the development and
refinement of AGI, emphasizing the integration of social, ethical, and
theoretical dimensions for future research. | Computers and Society |
What field is the article from? | Title: Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning
Abstract: In the realm of few-shot learning, foundation models like CLIP have proven
effective but exhibit limitations in cross-domain robustness especially in
few-shot settings. Recent works add text as an extra modality to enhance the
performance of these models. Most of these approaches treat text as an
auxiliary modality without fully exploring its potential to elucidate the
underlying class visual features distribution. In this paper, we present a
novel approach that leverages text-derived statistics to predict the mean and
covariance of the visual feature distribution for each class. This predictive
framework enriches the latent space, yielding more robust and generalizable
few-shot learning models. We demonstrate the efficacy of incorporating both
mean and covariance statistics in improving few-shot classification performance
across various datasets. Our method shows that we can use text to predict the
mean and covariance of the distribution offering promising improvements in
few-shot learning scenarios. | Computer Vision |
What field is the article from? | Title: Visual Explanations via Iterated Integrated Attributions
Abstract: We introduce Iterated Integrated Attributions (IIA) - a generic method for
explaining the predictions of vision models. IIA employs iterative integration
across the input image, the internal representations generated by the model,
and their gradients, yielding precise and focused explanation maps. We
demonstrate the effectiveness of IIA through comprehensive evaluations across
various tasks, datasets, and network architectures. Our results showcase that
IIA produces accurate explanation maps, outperforming other state-of-the-art
explanation techniques. | Computer Vision |
What field is the article from? | Title: Robust Fine-Tuning of Vision-Language Models for Domain Generalization
Abstract: Transfer learning enables the sharing of common knowledge among models for a
variety of downstream tasks, but traditional methods suffer in limited training
data settings and produce narrow models incapable of effectively generalizing
under distribution shifts. Foundation models have recently demonstrated
impressive zero-shot inference capabilities and robustness under distribution
shifts. However, zero-shot evaluation for these models has been predominantly
confined to benchmarks with simple distribution shifts, limiting our
understanding of their effectiveness under the more realistic shifts found in
practice. Moreover, common fine-tuning methods for these models have yet to be
evaluated against vision models in few-shot scenarios where training data is
limited. To address these gaps, we present a new recipe for few-shot
fine-tuning of the popular vision-language foundation model CLIP and evaluate
its performance on challenging benchmark datasets with realistic distribution
shifts from the WILDS collection. Our experimentation demonstrates that, while
zero-shot CLIP fails to match performance of trained vision models on more
complex benchmarks, few-shot CLIP fine-tuning outperforms its vision-only
counterparts in terms of in-distribution and out-of-distribution accuracy at
all levels of training data availability. This provides a strong incentive for
adoption of foundation models within few-shot learning applications operating
with real-world data. Code is available at
https://github.com/mit-ll/robust-vision-language-finetuning | Computer Vision |
What field is the article from? | Title: Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects
Abstract: Automatic assessment of impairment and disease severity is a key challenge in
data-driven medicine. We propose a novel framework to address this challenge,
which leverages AI models trained exclusively on healthy individuals. The
COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the
decrease in confidence of these models when presented with impaired or diseased
patients to quantify their deviation from the healthy population. We applied
the COBRA score to address a key limitation of current clinical evaluation of
upper-body impairment in stroke patients. The gold-standard Fugl-Meyer
Assessment (FMA) requires in-person administration by a trained assessor for
30-45 minutes, which restricts monitoring frequency and precludes physicians
from adapting rehabilitation protocols to the progress of each patient. The
COBRA score, computed automatically in under one minute, is shown to be
strongly correlated with the FMA on an independent test cohort for two
different data modalities: wearable sensors ($\rho = 0.845$, 95% CI
[0.743,0.908]) and video ($\rho = 0.746$, 95% C.I [0.594, 0.847]). To
demonstrate the generalizability of the approach to other conditions, the COBRA
score was also applied to quantify severity of knee osteoarthritis from
magnetic-resonance imaging scans, again achieving significant correlation with
an independent clinical assessment ($\rho = 0.644$, 95% C.I [0.585,0.696]). | Machine Learning |
What field is the article from? | Title: Optimizing IaC Configurations: a Case Study Using Nature-inspired Computing
Abstract: In the last years, one of the fields of artificial intelligence that has been
investigated the most is nature-inspired computing. The research done on this
specific topic showcases the interest that sparks in researchers and
practitioners, who put their focus on this paradigm because of the adaptability
and ability of nature-inspired algorithms to reach high-quality outcomes on a
wide range of problems. In fact, this kind of methods has been successfully
applied to solve real-world problems in heterogeneous fields such as medicine,
transportation, industry, or software engineering. Our main objective with this
paper is to describe a tool based on nature-inspired computing for solving a
specific software engineering problem. The problem faced consists of optimizing
Infrastructure as Code deployment configurations. For this reason, the name of
the system is IaC Optimizer Platform. A prototypical version of the IOP was
described in previous works, in which the functionality of this platform was
introduced. With this paper, we take a step forward by describing the final
release of the IOP, highlighting its main contribution regarding the current
state-of-the-art, and justifying the decisions made on its implementation.
Also, we contextualize the IOP within the complete platform in which it is
embedded, describing how a user can benefit from its use. To do that, we also
present and solve a real-world use case. | Software Engineering |
What field is the article from? | Title: Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation
Abstract: In Conversational Recommendation System (CRS), an agent is asked to recommend
a set of items to users within natural language conversations. To address the
need for both conversational capability and personalized recommendations, prior
works have utilized separate recommendation and dialogue modules. However, such
approach inevitably results in a discrepancy between recommendation results and
generated responses. To bridge the gap, we propose a multi-task learning for a
unified CRS, where a single model jointly learns both tasks via Contextualized
Knowledge Distillation (ConKD). We introduce two versions of ConKD: hard gate
and soft gate. The former selectively gates between two task-specific teachers,
while the latter integrates knowledge from both teachers. Our gates are
computed on-the-fly in a context-specific manner, facilitating flexible
integration of relevant knowledge. Extensive experiments demonstrate that our
single model significantly improves recommendation performance while enhancing
fluency, and achieves comparable results in terms of diversity. | Computational Linguistics |
What field is the article from? | Title: HeTriNet: Heterogeneous Graph Triplet Attention Network for Drug-Target-Disease Interaction
Abstract: Modeling the interactions between drugs, targets, and diseases is paramount
in drug discovery and has significant implications for precision medicine and
personalized treatments. Current approaches frequently consider drug-target or
drug-disease interactions individually, ignoring the interdependencies among
all three entities. Within human metabolic systems, drugs interact with protein
targets in cells, influencing target activities and subsequently impacting
biological pathways to promote healthy functions and treat diseases. Moving
beyond binary relationships and exploring tighter triple relationships is
essential to understanding drugs' mechanism of action (MoAs). Moreover,
identifying the heterogeneity of drugs, targets, and diseases, along with their
distinct characteristics, is critical to model these complex interactions
appropriately. To address these challenges, we effectively model the
interconnectedness of all entities in a heterogeneous graph and develop a novel
Heterogeneous Graph Triplet Attention Network (\texttt{HeTriNet}).
\texttt{HeTriNet} introduces a novel triplet attention mechanism within this
heterogeneous graph structure. Beyond pairwise attention as the importance of
an entity for the other one, we define triplet attention to model the
importance of pairs for entities in the drug-target-disease triplet prediction
problem. Experimental results on real-world datasets show that
\texttt{HeTriNet} outperforms several baselines, demonstrating its remarkable
proficiency in uncovering novel drug-target-disease relationships. | Machine Learning |
What field is the article from? | Title: Classification of Tabular Data by Text Processing
Abstract: Natural Language Processing technology has advanced vastly in the past
decade. Text processing has been successfully applied to a wide variety of
domains. In this paper, we propose a novel framework, Text Based
Classification(TBC), that uses state of the art text processing techniques to
solve classification tasks on tabular data. We provide a set of controlled
experiments where we present the benefits of using this approach against other
classification methods. Experimental results on several data sets also show
that this framework achieves comparable performance to that of several state of
the art models in accuracy, precision and recall of predicted classes. | Artificial Intelligence |
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