id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.00472 | Binned Spectral Power Loss for Improved Prediction of Chaotic Systems | cs.LG math.DS physics.flu-dyn | Forecasting multiscale chaotic dynamical systems with deep learning remains a
formidable challenge due to the spectral bias of neural networks, which hinders
the accurate representation of fine-scale structures in long-term predictions.
This issue is exacerbated when models are deployed autoregressively, leading to
c... |
2502.00473 | Weak-to-Strong Diffusion with Reflection | cs.LG cs.CV | The goal of diffusion generative models is to align the learned distribution
with the real data distribution through gradient score matching. However,
inherent limitations in training data quality, modeling strategies, and
architectural design lead to inevitable gap between generated outputs and real
data. To reduce ... |
2502.00474 | A framework for river connectivity classification using temporal image
processing and attention based neural networks | cs.CV cs.LG eess.IV | Measuring the connectivity of water in rivers and streams is essential for
effective water resource management. Increased extreme weather events
associated with climate change can result in alterations to river and stream
connectivity. While traditional stream flow gauges are costly to deploy and
limited to large riv... |
2502.00476 | Offshore wind farm layout optimization using mathematical programming
techniques | cs.CE | Offshore wind power is a renewable energy of growing relevance in current
electric energy systems, presenting favorable wind conditions in comparison
with the sites on land. However, the higher energy yield has to compensate the
increment in installation and maintenance costs, thus the importance of
optimizing resour... |
2502.00486 | Mixed extreme wave climate model for reanalysis databases | cs.CE | Hindcast or wave reanalysis databases (WRDB) constitute a powerful source
with respect to instrumental records in the design of offshore and coastal
structures, since they offer important advantages for the statistical
characterization of wave climate variables, such as continuous long time
records of significant wav... |
2502.00488 | Learn Sharp Interface Solution by Homotopy Dynamics | cs.LG cs.NA math.NA | Solving partial differential equations (PDEs) using neural networks has
become a central focus in scientific machine learning. Training neural networks
for sharp interface problems is particularly challenging due to certain
parameters in the PDEs that introduce near-singularities in the loss function.
In this study, ... |
2502.00490 | Oscillations Make Neural Networks Robust to Quantization | cs.LG | We challenge the prevailing view that oscillations in Quantization Aware
Training (QAT) are merely undesirable artifacts caused by the Straight-Through
Estimator (STE). Through theoretical analysis of QAT in linear models, we
demonstrate that the gradient of the loss function can be decomposed into two
terms: the ori... |
2502.00494 | Data Overvaluation Attack and Truthful Data Valuation | cs.CR cs.AI cs.LG | In collaborative machine learning, data valuation, i.e., evaluating the
contribution of each client' data to the machine learning model, has become a
critical task for incentivizing and selecting positive data contributions.
However, existing studies often assume that clients engage in data valuation
truthfully, over... |
2502.00495 | Looking into the Future of Health-Care Services: Can Life-Like Agents
Change the Future of Health-Care Services? | cs.CY cs.AI | Time constraints on doctor patient interaction and restricted access to
specialists under the managed care system led to increasingly referring to
computers as a medical information source and a self-health-care management
tool. However, research show that less than 40% of information seekers
indicated that online in... |
2502.00497 | Convolutional Fourier Analysis Network (CFAN): A Unified Time-Frequency
Approach for ECG Classification | cs.LG eess.SP | Machine learning has transformed the classification of biomedical signals
such as electrocardiograms (ECGs). Advances in deep learning, particularly
convolutional neural networks (CNNs), enable automatic feature extraction,
raising the question: Can combining time- and frequency-domain attributes
enhance classificati... |
2502.00498 | MetaOpenFOAM 2.0: Large Language Model Driven Chain of Thought for
Automating CFD Simulation and Post-Processing | cs.AI physics.comp-ph | Computational Fluid Dynamics (CFD) is widely used in aerospace, energy, and
biology to model fluid flow, heat transfer, and chemical reactions. While Large
Language Models (LLMs) have transformed various domains, their application in
CFD remains limited, particularly for complex tasks like post-processing. To
bridge ... |
2502.00499 | Discovering Directly-Follows Graph Model for Acyclic Processes | cs.AI | Process mining is the common name for a range of methods and approaches aimed
at analysing and improving processes. Specifically, methods that aim to derive
process models from event logs fall under the category of process discovery.
Within the range of processes, acyclic processes form a distinct category. In
such p... |
2502.00500 | Video Latent Flow Matching: Optimal Polynomial Projections for Video
Interpolation and Extrapolation | cs.CV cs.AI cs.LG | This paper considers an efficient video modeling process called Video Latent
Flow Matching (VLFM). Unlike prior works, which randomly sampled latent patches
for video generation, our method relies on current strong pre-trained image
generation models, modeling a certain caption-guided flow of latent patches
that can ... |
2502.00501 | Optimizing Feature Selection in Causal Inference: A Three-Stage
Computational Framework for Unbiased Estimation | stat.ME cs.AI cs.LG stat.ML | Feature selection is an important but challenging task in causal inference
for obtaining unbiased estimates of causal quantities. Properly selected
features in causal inference not only significantly reduce the time required to
implement a matching algorithm but, more importantly, can also reduce the bias
and varianc... |
2502.00507 | A statistically consistent measure of Semantic Variability using
Language Models | cs.CL cs.AI | To address the issue of variability in the output generated by a language
model, we present a measure of semantic variability that is statistically
consistent under mild assumptions. This measure, denoted as semantic spectral
entropy, is a easy to implement algorithm that requires just off the shelf
language models. ... |
2502.00510 | Who's the MVP? A Game-Theoretic Evaluation Benchmark for Modular
Attribution in LLM Agents | cs.AI cs.CL | Large Language Model (LLM) agents frameworks often employ modular
architectures, incorporating components such as planning, reasoning, action
execution, and reflection to tackle complex tasks. However, quantifying the
contribution of each module to overall system performance remains a significant
challenge, impeding ... |
2502.00511 | Bridging Internal Probability and Self-Consistency for Effective and
Efficient LLM Reasoning | cs.LG cs.AI cs.CL | Recent advancements in large language models (LLMs) have demonstrated
remarkable reasoning capabilities. However, single-shot inference often yields
unreliable results for complex reasoning tasks, leading researchers to explore
multiple reasoning paths through methods such as perplexity and
self-consistency. In this ... |
2502.00513 | Covariance Analysis of Attitude and Angular Rate Estimation using
Accelerometers | eess.SY cs.SY | In this work a method for using accelerometers for the determination of
angular velocity and acceleration is presented. Minimum sensor requirements and
insights into how an array of accelerometers can be configured to maximize
estimator performance are considered. The framework presented utilizes linear
least squares... |
2502.00519 | CoDocBench: A Dataset for Code-Documentation Alignment in Software
Maintenance | cs.SE cs.LG | One of the central tasks in software maintenance is being able to understand
and develop code changes. Thus, given a natural language description of the
desired new operation of a function, an agent (human or AI) might be asked to
generate the set of edits to that function to implement the desired new
operation; like... |
2502.00520 | Variance Reduction via Resampling and Experience Replay | stat.ML cs.LG | Experience replay is a foundational technique in reinforcement learning that
enhances learning stability by storing past experiences in a replay buffer and
reusing them during training. Despite its practical success, its theoretical
properties remain underexplored. In this paper, we present a theoretical
framework th... |
2502.00527 | PolarQuant: Leveraging Polar Transformation for Efficient Key Cache
Quantization and Decoding Acceleration | cs.LG cs.CL | The KV cache in large language models is a dominant factor in memory usage,
limiting their broader applicability. Quantizing the cache to lower bit widths
is an effective way to reduce computational costs; however, previous methods
struggle with quantizing key vectors due to outliers, resulting in excessive
overhead.... |
2502.00528 | Vision-Language Modeling in PET/CT for Visual Grounding of Positive
Findings | cs.CV cs.CL | Vision-language models can connect the text description of an object to its
specific location in an image through visual grounding. This has potential
applications in enhanced radiology reporting. However, these models require
large annotated image-text datasets, which are lacking for PET/CT. We developed
an automate... |
2502.00529 | Graph Data Management and Graph Machine Learning: Synergies and
Opportunities | cs.DB | The ubiquity of machine learning, particularly deep learning, applied to
graphs is evident in applications ranging from cheminformatics (drug discovery)
and bioinformatics (protein interaction prediction) to knowledge graph-based
query answering, fraud detection, and social network analysis. Concurrently,
graph data ... |
2502.00530 | Generic Multimodal Spatially Graph Network for Spatially Embedded
Network Representation Learning | cs.LG cs.AI cs.SI | Spatially embedded networks (SENs) represent a special type of complex graph,
whose topologies are constrained by the networks' embedded spatial
environments. The graph representation of such networks is thereby influenced
by the embedded spatial features of both nodes and edges. Accurate network
representation of th... |
2502.00532 | Enhancing Field-Oriented Control of Electric Drives with Tiny Neural
Network Optimized for Micro-controllers | cs.LG cs.SY eess.SY | The deployment of neural networks on resource-constrained micro-controllers
has gained momentum, driving many advancements in Tiny Neural Networks. This
paper introduces a tiny feed-forward neural network, TinyFC, integrated into
the Field-Oriented Control (FOC) of Permanent Magnet Synchronous Motors
(PMSMs). Proport... |
2502.00534 | Transition Transfer $Q$-Learning for Composite Markov Decision Processes | stat.ML cs.LG | To bridge the gap between empirical success and theoretical understanding in
transfer reinforcement learning (RL), we study a principled approach with
provable performance guarantees. We introduce a novel composite MDP framework
where high-dimensional transition dynamics are modeled as the sum of a low-rank
component... |
2502.00535 | Work-Efficient Parallel Non-Maximum Suppression Kernels | cs.CV cs.DC | In the context of object detection, sliding-window classifiers and
single-shot Convolutional Neural Network (CNN) meta-architectures typically
yield multiple overlapping candidate windows with similar high scores around
the true location of a particular object. Non-Maximum Suppression (NMS) is the
process of selectin... |
2502.00536 | CAD: Confidence-Aware Adaptive Displacement for Semi-Supervised Medical
Image Segmentation | cs.CV cs.LG | Semi-supervised medical image segmentation aims to leverage minimal expert
annotations, yet remains confronted by challenges in maintaining high-quality
consistency learning. Excessive perturbations can degrade alignment and hinder
precise decision boundaries, especially in regions with uncertain predictions.
In this... |
2502.00537 | Detecting Ambiguities to Guide Query Rewrite for Robust Conversations in
Enterprise AI Assistants | cs.CL | Multi-turn conversations with an Enterprise AI Assistant can be challenging
due to conversational dependencies in questions, leading to ambiguities and
errors. To address this, we propose an NLU-NLG framework for ambiguity
detection and resolution through reformulating query automatically and
introduce a new task cal... |
2502.00543 | VertiFormer: A Data-Efficient Multi-Task Transformer for Off-Road Robot
Mobility | cs.RO cs.CV cs.LG | Sophisticated learning architectures, e.g., Transformers, present a unique
opportunity for robots to understand complex vehicle-terrain kinodynamic
interactions for off-road mobility. While internet-scale data are available for
Natural Language Processing (NLP) and Computer Vision (CV) tasks to train
Transformers, re... |
2502.00545 | Integrating Frequency Guidance into Multi-source Domain Generalization
for Bearing Fault Diagnosis | cs.LG cs.AI cs.CV | Recent generalizable fault diagnosis researches have effectively tackled the
distributional shift between unseen working conditions. Most of them mainly
focus on learning domain-invariant representation through feature-level
methods. However, the increasing numbers of unseen domains may lead to
domain-invariant featu... |
2502.00547 | Milmer: a Framework for Multiple Instance Learning based Multimodal
Emotion Recognition | cs.CV cs.AI cs.HC | Emotions play a crucial role in human behavior and decision-making, making
emotion recognition a key area of interest in human-computer interaction (HCI).
This study addresses the challenges of emotion recognition by integrating
facial expression analysis with electroencephalogram (EEG) signals, introducing
a novel m... |
2502.00550 | Muti-Fidelity Prediction and Uncertainty Quantification with Laplace
Neural Operators for Parametric Partial Differential Equations | cs.LG cs.NA math.NA physics.comp-ph | Laplace Neural Operators (LNOs) have recently emerged as a promising approach
in scientific machine learning due to the ability to learn nonlinear maps
between functional spaces. However, this framework often requires substantial
amounts of high-fidelity (HF) training data, which is often prohibitively
expensive to a... |
2502.00552 | Optimal Sensor Placement in Power Transformers Using Physics-Informed
Neural Networks | cs.LG cs.SY eess.SY | Our work aims at simulating and predicting the temperature conditions inside
a power transformer using Physics-Informed Neural Networks (PINNs). The
predictions obtained are then used to determine the optimal placement for
temperature sensors inside the transformer under the constraint of a limited
number of sensors,... |
2502.00557 | Sampling Binary Data by Denoising through Score Functions | stat.ML cs.LG | Gaussian smoothing combined with a probabilistic framework for denoising via
the empirical Bayes formalism, i.e., the Tweedie-Miyasawa formula (TMF), are
the two key ingredients in the success of score-based generative models in
Euclidean spaces. Smoothing holds the key for easing the problem of learning
and sampling... |
2502.00558 | Asynchronous Cooperative Multi-Agent Reinforcement Learning with Limited
Communication | cs.MA | We consider the problem setting in which multiple autonomous agents must
cooperatively navigate and perform tasks in an unknown,
communication-constrained environment. Traditional multi-agent reinforcement
learning (MARL) approaches assume synchronous communications and perform poorly
in such environments. We propose... |
2502.00559 | Deep learning model for ECG reconstruction reveals the information
content of ECG leads | eess.SP cs.LG | This study introduces a deep learning model based on the U-net architecture
to reconstruct missing leads in electrocardiograms (ECGs). Using publicly
available datasets, the model was trained to regenerate 12-lead ECG data from
reduced lead configurations, demonstrating high accuracy in lead
reconstruction. The resul... |
2502.00562 | Assessment of ChatGPT for Engineering Statics Analysis | cs.CE | Large language models (LLMs) such as OpenAI's ChatGPT hold potential for
automating engineering analysis, yet their reliability in solving multi-step
statics problems remains uncertain. This study evaluates the performance of
ChatGPT-4o and ChatGPT-o1-preview on foundational statics tasks, from simple
calculations of... |
2502.00563 | Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for
Semantic Segmentation | cs.CV eess.IV | Recent advancements in deep neural networks have significantly enhanced the
performance of semantic segmentation. However, class imbalance and instance
imbalance remain persistent challenges, where smaller instances and thin
boundaries are often overshadowed by larger structures. To address the
multiscale nature of s... |
2502.00567 | Lessons for GenAI Literacy From a Field Study of Human-GenAI
Augmentation in the Workplace | cs.CY cs.AI | Generative artificial intelligence (GenAI) is increasingly becoming a part of
work practices across the technology industry and being used across a range of
industries. This has necessitated the need to better understand how GenAI is
being used by professionals in the field so that we can better prepare students
for ... |
2502.00568 | Generating crossmodal gene expression from cancer histopathology
improves multimodal AI predictions | cs.CV cs.AI cs.LG | Emerging research has highlighted that artificial intelligence based
multimodal fusion of digital pathology and transcriptomic features can improve
cancer diagnosis (grading/subtyping) and prognosis (survival risk) prediction.
However, such direct fusion for joint decision is impractical in real clinical
settings, wh... |
2502.00571 | Contrastive Forward-Forward: A Training Algorithm of Vision Transformer | cs.CV cs.LG | Although backpropagation is widely accepted as a training algorithm for
artificial neural networks, researchers are always looking for inspiration from
the brain to find ways with potentially better performance. Forward-Forward is
a new training algorithm that is more similar to what occurs in the brain,
although the... |
2502.00575 | DeepUKF-VIN: Adaptively-tuned Deep Unscented Kalman Filter for 3D
Visual-Inertial Navigation based on IMU-Vision-Net | cs.RO cs.SY eess.SY | This paper addresses the challenge of estimating the orientation, position,
and velocity of a vehicle operating in three-dimensional (3D) space with six
degrees of freedom (6-DoF). A Deep Learning-based Adaptation Mechanism (DLAM)
is proposed to adaptively tune the noise covariance matrices of Kalman-type
filters for... |
2502.00577 | Understanding Multimodal LLMs Under Distribution Shifts: An
Information-Theoretic Approach | cs.AI cs.CL cs.LG | Multimodal large language models (MLLMs) have shown promising capabilities
but struggle under distribution shifts, where evaluation data differ from
instruction tuning distributions. Although previous works have provided
empirical evaluations, we argue that establishing a formal framework that can
characterize and qu... |
2502.00580 | Defense Against the Dark Prompts: Mitigating Best-of-N Jailbreaking with
Prompt Evaluation | cs.CR cs.AI cs.CL cs.CY | Recent work showed Best-of-N (BoN) jailbreaking using repeated use of random
augmentations (such as capitalization, punctuation, etc) is effective against
all major large language models (LLMs). We have found that $100\%$ of the BoN
paper's successful jailbreaks (confidence interval $[99.65\%, 100.00\%]$) and
$99.8\%... |
2502.00581 | Trajectory Planning and Control for Differentially Flat Fixed-Wing
Aerial Systems | cs.RO | Efficient real-time trajectory planning and control for fixed-wing unmanned
aerial vehicles is challenging due to their non-holonomic nature, complex
dynamics, and the additional uncertainties introduced by unknown aerodynamic
effects. In this paper, we present a fast and efficient real-time trajectory
planning and c... |
2502.00582 | Uniform-in-time weak propagation of chaos for consensus-based
optimization | math.OC cs.LG math.PR | We study the uniform-in-time weak propagation of chaos for the
consensus-based optimization (CBO) method on a bounded searching domain. We
apply the methodology for studying long-time behaviors of interacting particle
systems developed in the work of Delarue and Tse (ArXiv:2104.14973). Our work
shows that the weak er... |
2502.00583 | Data-Driven Mispronunciation Pattern Discovery for Robust Speech
Recognition | cs.CL cs.SD eess.AS | Recent advancements in machine learning have significantly improved speech
recognition, but recognizing speech from non-fluent or accented speakers
remains a challenge. Previous efforts, relying on rule-based pronunciation
patterns, have struggled to fully capture non-native errors. We propose two
data-driven approac... |
2502.00585 | Converting Transformers into DGNNs Form | cs.LG cs.CL | Recent advances in deep learning have established Transformer architectures
as the predominant modeling paradigm. Central to the success of Transformers is
the self-attention mechanism, which scores the similarity between query and key
matrices to modulate a value matrix. This operation bears striking similarities
to... |
2502.00587 | Robust Knowledge Distillation in Federated Learning: Counteracting
Backdoor Attacks | cs.CR cs.AI | Federated Learning (FL) enables collaborative model training across multiple
devices while preserving data privacy. However, it remains susceptible to
backdoor attacks, where malicious participants can compromise the global model.
Existing defence methods are limited by strict assumptions on data
heterogeneity (Non-I... |
2502.00592 | M+: Extending MemoryLLM with Scalable Long-Term Memory | cs.CL | Equipping large language models (LLMs) with latent-space memory has attracted
increasing attention as they can extend the context window of existing language
models. However, retaining information from the distant past remains a
challenge. For example, MemoryLLM (Wang et al., 2024a), as a representative
work with lat... |
2502.00593 | Dominated Novelty Search: Rethinking Local Competition in
Quality-Diversity | cs.NE cs.LG | Quality-Diversity is a family of evolutionary algorithms that generate
diverse, high-performing solutions through local competition principles
inspired by natural evolution. While research has focused on improving specific
aspects of Quality-Diversity algorithms, surprisingly little attention has been
paid to investi... |
2502.00594 | Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing | cs.CV cs.AI | State Space Models (SSMs) with selective scan (Mamba) have been adapted into
efficient vision models. Mamba, unlike Vision Transformers, achieves linear
complexity for token interactions through a recurrent hidden state process.
This sequential processing is enhanced by a parallel scan algorithm, which
reduces the co... |
2502.00595 | RPGBENCH: Evaluating Large Language Models as Role-Playing Game Engines | cs.CL cs.AI | We present RPGBench, the first benchmark designed to evaluate large language
models (LLMs) as text-based role-playing game (RPG) engines. RPGBench comprises
two core tasks: Game Creation (GC) and Game Simulation (GS). In GC, an LLM must
craft a valid and playable RPG world using a structured event-state
representatio... |
2502.00596 | Rewinding the byte trail of the White Whale | cs.IT math.IT | Motivated by a popular code golf challenge, we review some key ideas from
information theory and discuss how to efficiently compress a streaming file
with an acceptable error rate.
|
2502.00601 | Enhancing Offline Reinforcement Learning with Curriculum Learning-Based
Trajectory Valuation | cs.LG | The success of deep reinforcement learning (DRL) relies on the availability
and quality of training data, often requiring extensive interactions with
specific environments. In many real-world scenarios, where data collection is
costly and risky, offline reinforcement learning (RL) offers a solution by
utilizing data ... |
2502.00602 | Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing | cs.CL cs.LG | Large language models (LLMs) have achieved remarkable performance on various
natural language tasks. However, they are trained on static corpora and their
knowledge can become outdated quickly in the fast-changing world. This
motivates the development of knowledge editing (KE) to update specific
knowledge in LLMs wit... |
2502.00604 | Gradient Alignment in Physics-informed Neural Networks: A Second-Order
Optimization Perspective | cs.LG cs.AI physics.comp-ph | Multi-task learning through composite loss functions is fundamental to modern
deep learning, yet optimizing competing objectives remains challenging. We
present new theoretical and practical approaches for addressing directional
conflicts between loss terms, demonstrating their effectiveness in
physics-informed neura... |
2502.00605 | The Query/Hit Model for Sequential Hypothesis Testing | cs.IT cs.LG eess.SP math.IT | This work introduces the Query/Hit (Q/H) learning model. The setup consists
of two agents. One agent, Alice, has access to a streaming source, while the
other, Bob, does not have direct access to the source. Communication occurs
through sequential Q/H pairs: Bob sends a sequence of source symbols (queries),
and Alice... |
2502.00607 | PAC Learning is just Bipartite Matching (Sort of) | cs.LG cs.DS stat.ML | The main goal of this article is to convince you, the reader, that supervised
learning in the Probably Approximately Correct (PAC) model is closely related
to -- of all things -- bipartite matching! En-route from PAC learning to
bipartite matching, I will overview a particular transductive model of
learning, and asso... |
2502.00611 | Enhancing Code Consistency in AI Research with Large Language Models and
Retrieval-Augmented Generation | cs.SE cs.AI | Ensuring that code accurately reflects the algorithms and methods described
in research papers is critical for maintaining credibility and fostering trust
in AI research. This paper presents a novel system designed to verify code
implementations against the algorithms and methodologies outlined in
corresponding resea... |
2502.00612 | Using Causality for Enhanced Prediction of Web Traffic Time Series | cs.LG cs.NI | Predicting web service traffic has significant social value, as it can be
applied to various practical scenarios, including but not limited to dynamic
resource scaling, load balancing, system anomaly detection, service-level
agreement compliance, and fraud detection. Web service traffic is characterized
by frequent a... |
2502.00617 | Efficient Language Modeling for Low-Resource Settings with Hybrid
RNN-Transformer Architectures | cs.CL | Transformer-based language models have recently been at the forefront of
active research in text generation. However, these models' advances come at the
price of prohibitive training costs, with parameter counts in the billions and
compute requirements measured in petaflop/s-decades. In this paper, we
investigate tra... |
2502.00618 | DesCLIP: Robust Continual Adaptation via General Attribute Descriptions
for Pretrained Vision-Language Models | cs.CV cs.AI | Continual adaptation of vision-language models (VLMs) focuses on leveraging
cross-modal pretrained knowledge to incrementally adapt for expanding
downstream tasks and datasets, while tackling the challenge of knowledge
forgetting. Existing research often focuses on connecting visual features with
specific class text ... |
2502.00619 | Distribution-aware Fairness Learning in Medical Image Segmentation From
A Control-Theoretic Perspective | eess.IV cs.AI cs.CV | Ensuring fairness in medical image segmentation is critical due to biases in
imbalanced clinical data acquisition caused by demographic attributes (e.g.,
age, sex, race) and clinical factors (e.g., disease severity). To address these
challenges, we introduce Distribution-aware Mixture of Experts (dMoE), inspired
by o... |
2502.00620 | Representations Shape Weak-to-Strong Generalization: Theoretical
Insights and Empirical Predictions | cs.LG cs.AI | Weak-to-Strong Generalization (W2SG), where a weak model supervises a
stronger one, serves as an important analogy for understanding how humans might
guide superhuman intelligence in the future. Promising empirical results
revealed that a strong model can surpass its weak supervisor. While recent work
has offered the... |
2502.00622 | Strengthening Generative Robot Policies through Predictive World
Modeling | cs.RO cs.CV cs.LG | We present generative predictive control (GPC), a learning control framework
that (i) clones a generative diffusion-based policy from expert demonstrations,
(ii) trains a predictive action-conditioned world model from both expert
demonstrations and random explorations, and (iii) synthesizes an online planner
that ran... |
2502.00627 | Discord Unveiled: A Comprehensive Dataset of Public Communication
(2015-2024) | cs.SI cs.DB | Discord has evolved from a gaming-focused communication tool into a versatile
platform supporting diverse online communities. Despite its large user base and
active public servers, academic research on Discord remains limited due to data
accessibility challenges. This paper introduces Discord Unveiled: A
Comprehensiv... |
2502.00629 | Advanced Weakly-Supervised Formula Exploration for Neuro-Symbolic
Mathematical Reasoning | cs.AI cs.LG | In recent years, neuro-symbolic methods have become a popular and powerful
approach that augments artificial intelligence systems with the capability to
perform abstract, logical, and quantitative deductions with enhanced precision
and controllability. Recent studies successfully performed symbolic reasoning
by lever... |
2502.00630 | Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM
Adaptation | cs.CV | Segment Anything Model (SAM) has demonstrated impressive zero-shot
performance and brought a range of unexplored capabilities to natural image
segmentation tasks. However, as a very important branch of image segmentation,
the performance of SAM remains uncertain when applied to medical image
segmentation due to the s... |
2502.00631 | MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density
Prediction | cs.CV | Bone density prediction via CT scans to estimate T-scores is crucial,
providing a more precise assessment of bone health compared to traditional
methods like X-ray bone density tests, which lack spatial resolution and the
ability to detect localized changes. However, CT-based prediction faces two
major challenges: th... |
2502.00633 | Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary
Tasks | cs.AI | Monte Carlo Tree Search (MCTS) has proven highly effective in solving complex
planning tasks by balancing exploration and exploitation using Upper Confidence
Bound for Trees (UCT). However, existing work have not considered MCTS-based
lifelong planning, where an agent faces a non-stationary series of tasks --
e.g., w... |
2502.00634 | SimulPL: Aligning Human Preferences in Simultaneous Machine Translation | cs.CL cs.AI | Simultaneous Machine Translation (SiMT) generates translations while
receiving streaming source inputs. This requires the SiMT model to learn a
read/write policy, deciding when to translate and when to wait for more source
input. Numerous linguistic studies indicate that audiences in SiMT scenarios
have distinct pref... |
2502.00639 | Zeroth-order Informed Fine-Tuning for Diffusion Model: A Recursive
Likelihood Ratio Optimizer | cs.CV cs.AI cs.LG stat.ML | The probabilistic diffusion model (DM), generating content by inferencing
through a recursive chain structure, has emerged as a powerful framework for
visual generation. After pre-training on enormous unlabeled data, the model
needs to be properly aligned to meet requirements for downstream applications.
How to effic... |
2502.00640 | CollabLLM: From Passive Responders to Active Collaborators | cs.AI | Large Language Models are typically trained with next-turn rewards, limiting
their ability to optimize for long-term interaction. As a result, they often
respond passively to ambiguous or open-ended user requests, failing to help
users reach their ultimate intents and leading to inefficient conversations. To
address ... |
2502.00641 | Evaluating Small Language Models for News Summarization: Implications
and Factors Influencing Performance | cs.CL cs.AI | The increasing demand for efficient summarization tools in
resource-constrained environments highlights the need for effective solutions.
While large language models (LLMs) deliver superior summarization quality,
their high computational resource requirements limit practical use
applications. In contrast, small langu... |
2502.00645 | General Coded Computing in a Probabilistic Straggler Regime | cs.DC cs.LG | Coded computing has demonstrated promising results in addressing straggler
resiliency in distributed computing systems. However, most coded computing
schemes are designed for exact computation, requiring the number of responding
servers to exceed a certain recovery threshold. Additionally, these schemes are
tailored ... |
2502.00646 | TrojanTime: Backdoor Attacks on Time Series Classification | cs.CR cs.AI cs.LG | Time Series Classification (TSC) is highly vulnerable to backdoor attacks,
posing significant security threats. Existing methods primarily focus on data
poisoning during the training phase, designing sophisticated triggers to
improve stealthiness and attack success rate (ASR). However, in practical
scenarios, attacke... |
2502.00648 | Agency in the Age of AI | cs.AI cs.MA | There is significant concern about the impact of generative AI on society.
Modern AI tools are capable of generating ever more realistic text, images, and
videos, and functional code, from minimal prompts. Accompanying this rise in
ability and usability, there is increasing alarm about the misuses to which
these tool... |
2502.00652 | Reformulation is All You Need: Addressing Malicious Text Features in
DNNs | cs.LG cs.CL cs.CR | Human language encompasses a wide range of intricate and diverse implicit
features, which attackers can exploit to launch adversarial or backdoor
attacks, compromising DNN models for NLP tasks. Existing model-oriented
defenses often require substantial computational resources as model size
increases, whereas sample-o... |
2502.00654 | EmoTalkingGaussian: Continuous Emotion-conditioned Talking Head
Synthesis | cs.CV | 3D Gaussian splatting-based talking head synthesis has recently gained
attention for its ability to render high-fidelity images with real-time
inference speed. However, since it is typically trained on only a short video
that lacks the diversity in facial emotions, the resultant talking heads
struggle to represent a ... |
2502.00657 | LLM Safety Alignment is Divergence Estimation in Disguise | cs.LG cs.AI cs.CY stat.ML | We propose a theoretical framework demonstrating that popular Large Language
Model (LLM) alignment methods, including Reinforcement Learning from Human
Feedback (RLHF) and alternatives, fundamentally function as divergence
estimators between aligned (preferred or safe) and unaligned (less-preferred or
harmful) distri... |
2502.00661 | EKF-Based Radar-Inertial Odometry with Online Temporal Calibration | cs.RO | Accurate time synchronization between heterogeneous sensors is crucial for
ensuring robust state estimation in multi-sensor fusion systems. Sensor delays
often cause discrepancies between the actual time when the event was captured
and the time of sensor measurement, leading to temporal misalignment (time
offset) bet... |
2502.00662 | Mitigating the Modality Gap: Few-Shot Out-of-Distribution Detection with
Multi-modal Prototypes and Image Bias Estimation | cs.CV cs.CL cs.LG | Existing vision-language model (VLM)-based methods for out-of-distribution
(OOD) detection typically rely on similarity scores between input images and
in-distribution (ID) text prototypes. However, the modality gap between image
and text often results in high false positive rates, as OOD samples can exhibit
high sim... |
2502.00663 | Enhanced Convolutional Neural Networks for Improved Image Classification | cs.CV cs.AI | Image classification is a fundamental task in computer vision with diverse
applications, ranging from autonomous systems to medical imaging. The CIFAR-10
dataset is a widely used benchmark to evaluate the performance of
classification models on small-scale, multi-class datasets. Convolutional
Neural Networks (CNNs) h... |
2502.00665 | Cross-Modal Synergies: Unveiling the Potential of Motion-Aware Fusion
Networks in Handling Dynamic and Static ReID Scenarios | cs.CV | Navigating the complexities of person re-identification (ReID) in varied
surveillance scenarios, particularly when occlusions occur, poses significant
challenges. We introduce an innovative Motion-Aware Fusion (MOTAR-FUSE) network
that utilizes motion cues derived from static imagery to significantly enhance
ReID cap... |
2502.00666 | Avoiding $\mathbf{exp(R_{max})}$ scaling in RLHF through
Preference-based Exploration | cs.LG cs.AI stat.ML | Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal
technique for large language model (LLM) alignment. This paper studies the
setting of online RLHF and focus on improving sample efficiency. All existing
algorithms in online RLHF, whether doing passive exploration or active
exploration, suffer... |
2502.00669 | Safety Alignment Depth in Large Language Models: A Markov Chain
Perspective | cs.LG | Large Language Models (LLMs) are increasingly adopted in high-stakes
scenarios, yet their safety mechanisms often remain fragile. Simple jailbreak
prompts or even benign fine-tuning can bypass these protocols, underscoring the
need to understand where and how they fail. Recent findings suggest that
vulnerabilities em... |
2502.00672 | Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of
Model Prediction and Scientific Understanding of Soil Organic Carbon | physics.geo-ph cs.AI | Big data and the rapid development of artificial intelligence (AI) provide
unprecedented opportunities to enhance our understanding of the global carbon
cycle and other biogeochemical processes. However, retrieving mechanistic
knowledge from big data remains a challenge. Here, we develop a
Biogeochemistry-Informed Ne... |
2502.00673 | Retracted Citations and Self-citations in Retracted Publications: A
Comparative Study of Plagiarism and Fake Peer Review | cs.IR | Retracted citations remain a significant concern in academia as they
perpetuate misinformation and compromise the integrity of scientific literature
despite their invalidation. To analyze the impact of retracted citations, we
focused on two retraction categories: plagiarism and fake peer review. The data
set was sour... |
2502.00674 | Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models
Beneficial? | cs.CL cs.LG | Ensembling outputs from diverse sources is a straightforward yet effective
approach to boost performance. Mixture-of-Agents (MoA) is one such popular
ensemble method that aggregates outputs from multiple different Large Language
Models (LLMs). This paper raises the question in the context of language
models: is mixin... |
2502.00675 | ReFoRCE: A Text-to-SQL Agent with Self-Refinement, Format Restriction,
and Column Exploration | cs.CL | Text-to-SQL systems have unlocked easier access to critical data insights by
enabling natural language queries over structured databases. However, deploying
such systems in enterprise environments remains challenging due to factors such
as large, complex schemas (> 3000 columns), diverse SQL dialects (e.g.,
BigQuery,... |
2502.00677 | LLM-based event log analysis techniques: A survey | cs.AI cs.CR | Event log analysis is an important task that security professionals
undertake. Event logs record key information on activities that occur on
computing devices, and due to the substantial number of events generated, they
consume a large amount of time and resources to analyse. This demanding and
repetitive task is als... |
2502.00678 | How Contaminated Is Your Benchmark? Quantifying Dataset Leakage in Large
Language Models with Kernel Divergence | cs.LG cs.AI cs.CL | Dataset contamination, where evaluation datasets overlap with pre-training
corpora, inflates performance metrics and undermines the reliability of model
evaluations. Quantifying dataset contamination thus becomes essential to ensure
that performance evaluations genuinely reflect a model's ability to generalize
to uns... |
2502.00681 | A Survey of Quantized Graph Representation Learning: Connecting Graph
Structures with Large Language Models | cs.LG cs.AI cs.CL | Recent years have witnessed rapid advances in graph representation learning,
with the continuous embedding approach emerging as the dominant paradigm.
However, such methods encounter issues regarding parameter efficiency,
interpretability, and robustness. Thus, Quantized Graph Representation (QGR)
learning has recent... |
2502.00682 | Guidance Source Matters: How Guidance from AI, Expert, or a Group of
Analysts Impacts Visual Data Preparation and Analysis | cs.HC cs.AI | The progress in generative AI has fueled AI-powered tools like co-pilots and
assistants to provision better guidance, particularly during data analysis.
However, research on guidance has not yet examined the perceived efficacy of
the source from which guidance is offered and the impact of this source on the
user's pe... |
2502.00683 | IEEEICM25: "Stability of Digital Robust Motion Control Systems with
Disturbance Observer" | eess.SY cs.SY | In this paper, new stability analysis methods are proposed for digital robust
motion control systems implemented using a disturbance observer.
|
2502.00684 | Compositional Concept-Based Neuron-Level Interpretability for Deep
Reinforcement Learning | cs.LG cs.AI | Deep reinforcement learning (DRL), through learning policies or values
represented by neural networks, has successfully addressed many complex control
problems. However, the neural networks introduced by DRL lack interpretability
and transparency. Current DRL interpretability methods largely treat neural
networks as ... |
2502.00685 | IEEEICM25: "A High-Performance Disturbance Observer" | eess.SY cs.RO cs.SY | This paper proposes a novel Disturbance Observer, termed the High-Performance
Disturbance Observer, which achieves more accurate disturbance estimation
compared to the conventional disturbance observer, thereby delivering
significant improvements in robustness and performance for motion control
systems.
|
2502.00686 | Improved Community Detection using Stochastic Block Models | cs.SI | Identifying edge-dense communities that are also well-connected is an
important aspect of understanding community structure. Prior work has shown
that community detection methods can produce poorly connected communities, and
some can even produce internally disconnected communities. In this study we
evaluate the conn... |
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