id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.12400 | Interpolation pour l'augmentation de donnees : Application \`a la
gestion des adventices de la canne a sucre a la Reunion | [
"q-bio.QM",
"cs.LG",
"stat.AP",
"stat.ME",
"stat.ML"
] | Data augmentation is a crucial step in the development of robust supervised learning models, especially when dealing with limited datasets. This study explores interpolation techniques for the augmentation of geo-referenced data, with the aim of predicting the presence of Commelina benghalensis L. in sugarcane plots in... | {
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2501.12405 | Scopes of Alignment | [
"cs.CY",
"cs.AI",
"cs.CL"
] | Much of the research focus on AI alignment seeks to align large language models and other foundation models to the context-less and generic values of helpfulness, harmlessness, and honesty. Frontier model providers also strive to align their models with these values. In this paper, we motivate why we need to move beyon... | {
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2501.12407 | The Streaming Batch Model for Efficient and Fault-Tolerant Heterogeneous
Execution | [
"cs.DC",
"cs.LG"
] | While ML model training and inference are both GPU-intensive, CPU-based data processing is often the bottleneck. Distributed data processing systems based on the batch or stream processing models assume homogeneous resource requirements. They excel at CPU-based computation but either under-utilize heterogeneous resourc... | {
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2501.12408 | Control-ITRA: Controlling the Behavior of a Driving Model | [
"cs.AI",
"cs.LG",
"cs.RO",
"cs.SY",
"eess.SY",
"stat.ML"
] | Simulating realistic driving behavior is crucial for developing and testing autonomous systems in complex traffic environments. Equally important is the ability to control the behavior of simulated agents to tailor scenarios to specific research needs and safety considerations. This paper extends the general-purpose mu... | {
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2501.12415 | Comparative Analysis of Hand-Crafted and Machine-Driven
Histopathological Features for Prostate Cancer Classification and
Segmentation | [
"eess.IV",
"cs.CV",
"cs.LG",
"q-bio.QM"
] | Histopathological image analysis is a reliable method for prostate cancer identification. In this paper, we present a comparative analysis of two approaches for segmenting glandular structures in prostate images to automate Gleason grading. The first approach utilizes a hand-crafted learning technique, combining Gray L... | {
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2501.12417 | Egoistic MDS-based Rigid Body Localization | [
"cs.RO",
"eess.SP"
] | We consider a novel anchorless rigid body localization (RBL) suitable for application in autonomous driving (AD), in so far as the algorithm enables a rigid body to egoistically detect the location (relative translation) and orientation (relative rotation) of another body, without knowledge of the shape of the latter, ... | {
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2501.12418 | ImageRef-VL: Enabling Contextual Image Referencing in Vision-Language
Models | [
"cs.CV",
"cs.AI"
] | Vision-Language Models (VLMs) have demonstrated remarkable capabilities in understanding multimodal inputs and have been widely integrated into Retrieval-Augmented Generation (RAG) based conversational systems. While current VLM-powered chatbots can provide textual source references in their responses, they exhibit sig... | {
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2501.12419 | Ensemble score filter with image inpainting for data assimilation in
tracking surface quasi-geostrophic dynamics with partial observations | [
"physics.ao-ph",
"cs.LG",
"physics.data-an",
"physics.flu-dyn",
"stat.ML"
] | Data assimilation plays a pivotal role in understanding and predicting turbulent systems within geoscience and weather forecasting, where data assimilation is used to address three fundamental challenges, i.e., high-dimensionality, nonlinearity, and partial observations. Recent advances in machine learning (ML)-based d... | {
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2501.12420 | Consolidating TinyML Lifecycle with Large Language Models: Reality,
Illusion, or Opportunity? | [
"cs.SE",
"cs.AI",
"cs.LG"
] | The evolving requirements of Internet of Things (IoT) applications are driving an increasing shift toward bringing intelligence to the edge, enabling real-time insights and decision-making within resource-constrained environments. Tiny Machine Learning (TinyML) has emerged as a key enabler of this evolution, facilitati... | {
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2501.12421 | Tackling Small Sample Survival Analysis via Transfer Learning: A Study
of Colorectal Cancer Prognosis | [
"cs.LG",
"cs.AI",
"q-bio.QM"
] | Survival prognosis is crucial for medical informatics. Practitioners often confront small-sized clinical data, especially cancer patient cases, which can be insufficient to induce useful patterns for survival predictions. This study deals with small sample survival analysis by leveraging transfer learning, a useful mac... | {
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2501.12422 | CroMe: Multimodal Fake News Detection using Cross-Modal Tri-Transformer
and Metric Learning | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Multimodal Fake News Detection has received increasing attention recently. Existing methods rely on independently encoded unimodal data and overlook the advantages of capturing intra-modality relationships and integrating inter-modal similarities using advanced techniques. To address these issues, Cross-Modal Tri-Trans... | {
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2501.12423 | FREYR: A Framework for Recognizing and Executing Your Requests | [
"cs.SE",
"cs.AI"
] | Large language models excel as conversational agents, but their capabilities can be further extended through tool usage, i.e.: executable code, to enhance response accuracy or address specialized domains. Current approaches to enable tool usage often rely on model-specific prompting or fine-tuning a model for function-... | {
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2501.12424 | Multi-Modality Collaborative Learning for Sentiment Analysis | [
"cs.LG",
"cs.AI",
"cs.IR"
] | Multimodal sentiment analysis (MSA) identifies individuals' sentiment states in videos by integrating visual, audio, and text modalities. Despite progress in existing methods, the inherent modality heterogeneity limits the effective capture of interactive sentiment features across modalities. In this paper, by introduc... | {
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2501.12425 | Multi-stage intermediate fusion for multimodal learning to classify
non-small cell lung cancer subtypes from CT and PET | [
"eess.IV",
"cs.AI",
"cs.CV",
"q-bio.QM"
] | Accurate classification of histological subtypes of non-small cell lung cancer (NSCLC) is essential in the era of precision medicine, yet current invasive techniques are not always feasible and may lead to clinical complications. This study presents a multi-stage intermediate fusion approach to classify NSCLC subtypes ... | {
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2501.12427 | SafePowerGraph-HIL: Real-Time HIL Validation of Heterogeneous GNNs for
Bridging Sim-to-Real Gap in Power Grids | [
"cs.LG",
"cs.AI"
] | As machine learning (ML) techniques gain prominence in power system research, validating these methods' effectiveness under real-world conditions requires real-time hardware-in-the-loop (HIL) simulations. HIL simulation platforms enable the integration of computational models with physical devices, allowing rigorous te... | {
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2501.12428 | SplitQuant: Layer Splitting for Low-Bit Neural Network Quantization | [
"cs.LG",
"cs.AI"
] | Quantization for deep neural networks (DNNs) is the process of mapping the parameter values of DNNs from original data types to other data types of lower precision to reduce model sizes and make inference faster. Quantization often maps different original values to a single quantized value because the range of the orig... | {
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2501.12429 | Fuel Efficiency Analysis of the Public Transportation System Based on
the Gaussian Mixture Model Clustering | [
"cs.LG",
"cs.AI"
] | Public transportation is a major source of greenhouse gas emissions, highlighting the need to improve bus fuel efficiency. Clustering algorithms assist in analyzing fuel efficiency by grouping data into clusters, but irrelevant features may complicate the analysis and choosing the optimal number of clusters remains a c... | {
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2501.12430 | SCFCRC: Simultaneously Counteract Feature Camouflage and Relation
Camouflage for Fraud Detection | [
"cs.LG",
"cs.AI"
] | In fraud detection, fraudsters often interact with many benign users, camouflaging their features or relations to hide themselves. Most existing work concentrates solely on either feature camouflage or relation camouflage, or decoupling feature learning and relation learning to avoid the two camouflage from affecting e... | {
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2501.12431 | Modality Interactive Mixture-of-Experts for Fake News Detection | [
"cs.LG",
"cs.AI",
"cs.CL"
] | The proliferation of fake news on social media platforms disproportionately impacts vulnerable populations, eroding trust, exacerbating inequality, and amplifying harmful narratives. Detecting fake news in multimodal contexts -- where deceptive content combines text and images -- is particularly challenging due to the ... | {
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2501.12432 | Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel
Tool Invocation | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Although current Large Language Models (LLMs) exhibit impressive capabilities, performing complex real-world tasks still requires tool learning. Mainstream methods, such as CoT/ReAct, rely on step-by-step tool invocation to interact with external environments, but they are limited in perceptual scope and lack adequate ... | {
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2501.12433 | Owls are wise and foxes are unfaithful: Uncovering animal stereotypes in
vision-language models | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Animal stereotypes are deeply embedded in human culture and language. They often shape our perceptions and expectations of various species. Our study investigates how animal stereotypes manifest in vision-language models during the task of image generation. Through targeted prompts, we explore whether DALL-E perpetuate... | {
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2501.12434 | Enhancing Retrosynthesis with Conformer: A Template-Free Method | [
"cs.LG",
"cs.AI"
] | Retrosynthesis plays a crucial role in the fields of organic synthesis and drug development, where the goal is to identify suitable reactants that can yield a target product molecule. Although existing methods have achieved notable success, they typically overlook the 3D conformational details and internal spatial orga... | {
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2501.12447 | Tight relations and equivalences between smooth relative entropies | [
"quant-ph",
"cs.IT",
"math-ph",
"math.IT",
"math.MP"
] | The precise one-shot characterisation of operational tasks in classical and quantum information theory relies on different forms of smooth entropic quantities. A particularly important connection is between the hypothesis testing relative entropy and the smoothed max-relative entropy, which together govern many operati... | {
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2501.12456 | Deploying Privacy Guardrails for LLMs: A Comparative Analysis of
Real-World Applications | [
"cs.CR",
"cs.AI",
"cs.LG",
"cs.SE"
] | The adoption of Large Language Models (LLMs) has revolutionized AI applications but poses significant challenges in safeguarding user privacy. Ensuring compliance with privacy regulations such as GDPR and CCPA while addressing nuanced privacy risks requires robust and scalable frameworks. This paper presents a detailed... | {
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2501.12465 | Adaptive PII Mitigation Framework for Large Language Models | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Artificial Intelligence (AI) faces growing challenges from evolving data protection laws and enforcement practices worldwide. Regulations like GDPR and CCPA impose strict compliance requirements on Machine Learning (ML) models, especially concerning personal data use. These laws grant individuals rights such as data co... | {
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2501.12473 | RIS-Aided Monitoring With Cooperative Jamming: Design and Performance
Analysis | [
"eess.SY",
"cs.SY"
] | We investigate a reconfigurable intelligent surface (RIS) aided wireless surveillance system. In this system, a monitor not only receives signal from suspicious transmitter via a RIS-enhanced legitimate surveillance (LS) link but also simultaneously takes control of multiple jammers to degrade the quality of received s... | {
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2501.12477 | Slot-BERT: Self-supervised Object Discovery in Surgical Video | [
"eess.IV",
"cs.CV"
] | Object-centric slot attention is a powerful framework for unsupervised learning of structured and explainable representations that can support reasoning about objects and actions, including in surgical videos. While conventional object-centric methods for videos leverage recurrent processing to achieve efficiency, they... | {
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2501.12479 | Degree-Based Logical Adjacency Checking (DBLAC): A Novel Heuristic for
Vertex Coloring | [
"cs.DM",
"cs.AI"
] | Degree Based Logical Adjacency Checking (DBLAC). An efficient coloring of graphs with unique logical AND operations. The logical AND operation shows more effective color assignment and fewer number of induced colors in the case of common edges between vertices. In this work, we provide a detailed theoretical analysis o... | {
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2501.12482 | TOFFE -- Temporally-binned Object Flow from Events for High-speed and
Energy-Efficient Object Detection and Tracking | [
"cs.CV",
"cs.ET",
"cs.LG",
"cs.NE",
"cs.RO"
] | Object detection and tracking is an essential perception task for enabling fully autonomous navigation in robotic systems. Edge robot systems such as small drones need to execute complex maneuvers at high-speeds with limited resources, which places strict constraints on the underlying algorithms and hardware. Tradition... | {
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2501.12483 | A Smart IoT Framework for Climate-Resilient and Sustainable Maize
Farming In Uganda | [
"cs.CE"
] | This study provides a framework that incorporates the Internet of Things (IoT) technology into maize farming activities in Central Uganda as a solution to various challenges including climate change, sub-optimal resource use and low crop yields. Using IoT-based modeling and simulation, the presented solution recommends... | {
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2501.12485 | R2D2: Remembering, Reflecting and Dynamic Decision Making for Web Agents | [
"cs.AI"
] | The proliferation of web agents necessitates advanced navigation and interaction strategies within complex web environments. Current models often struggle with efficient navigation and action execution due to limited visibility and understanding of web structures. Our proposed R2D2 framework addresses these challenges ... | {
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2501.12486 | The Journey Matters: Average Parameter Count over Pre-training Unifies
Sparse and Dense Scaling Laws | [
"cs.LG",
"cs.CL"
] | Pruning eliminates unnecessary parameters in neural networks; it offers a promising solution to the growing computational demands of large language models (LLMs). While many focus on post-training pruning, sparse pre-training--which combines pruning and pre-training into a single phase--provides a simpler alternative. ... | {
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2501.12487 | fabSAM: A Farmland Boundary Delineation Method Based on the Segment
Anything Model | [
"cs.CV",
"cs.AI",
"eess.IV"
] | Delineating farmland boundaries is essential for agricultural management such as crop monitoring and agricultural census. Traditional methods using remote sensing imagery have been efficient but limited in generalisation. The Segment Anything Model (SAM), known for its impressive zero shot performance, has been adapted... | {
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2501.12488 | Bidirectional Brain Image Translation using Transfer Learning from
Generic Pre-trained Models | [
"eess.IV",
"cs.CV",
"q-bio.TO"
] | Brain imaging plays a crucial role in the diagnosis and treatment of various neurological disorders, providing valuable insights into the structure and function of the brain. Techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) enable non-invasive visualization of the brain, aiding in the un... | {
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2501.12489 | Large-image Object Detection for Fine-grained Recognition of Punches
Patterns in Medieval Panel Painting | [
"cs.CV",
"cs.AI",
"cs.LG"
] | The attribution of the author of an art piece is typically a laborious manual process, usually relying on subjective evaluations of expert figures. However, there are some situations in which quantitative features of the artwork can support these evaluations. The extraction of these features can sometimes be automated,... | {
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2501.12491 | Optimizing Blockchain Analysis: Tackling Temporality and Scalability
with an Incremental Approach with Metropolis-Hastings Random Walks | [
"cs.CE",
"stat.ML"
] | Blockchain technology, with implications in the financial domain, offers data in the form of large-scale transaction networks. Analyzing transaction networks facilitates fraud detection, market analysis, and supports government regulation. Despite many graph representation learning methods for transaction network analy... | {
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2501.12493 | ELEGNT: Expressive and Functional Movement Design for
Non-anthropomorphic Robot | [
"cs.RO",
"cs.HC"
] | Nonverbal behaviors such as posture, gestures, and gaze are essential for conveying internal states, both consciously and unconsciously, in human interaction. For robots to interact more naturally with humans, robot movement design should likewise integrate expressive qualities, such as intention, attention, and emotio... | {
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2501.12500 | Identification of Nonparametric Dynamic Causal Structure and Latent
Process in Climate System | [
"cs.LG",
"stat.ME"
] | The study of learning causal structure with latent variables has advanced the understanding of the world by uncovering causal relationships and latent factors, e.g., Causal Representation Learning (CRL). However, in real-world scenarios, such as those in climate systems, causal relationships are often nonparametric, dy... | {
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2501.12502 | Sequence Spreading-Based Semantic Communication Under High RF
Interference | [
"cs.NI",
"cs.LG"
] | In the evolving landscape of wireless communications, semantic communication (SemCom) has recently emerged as a 6G enabler that prioritizes the transmission of meaning and contextual relevance over conventional bit-centric metrics. However, the deployment of SemCom systems in industrial settings presents considerable c... | {
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2501.12508 | The Finite Element Neural Network Method: One Dimensional Study | [
"cs.CE",
"cs.AI"
] | The potential of neural networks (NN) in engineering is rooted in their capacity to understand intricate patterns and complex systems, leveraging their universal nonlinear approximation capabilities and high expressivity. Meanwhile, conventional numerical methods, backed by years of meticulous refinement, continue to b... | {
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2501.12516 | Robustness of Selected Learning Models under Label-Flipping Attack | [
"cs.LG",
"cs.CR"
] | In this paper we compare traditional machine learning and deep learning models trained on a malware dataset when subjected to adversarial attack based on label-flipping. Specifically, we investigate the robustness of Support Vector Machines (SVM), Random Forest, Gaussian Naive Bayes (GNB), Gradient Boosting Machine (GB... | {
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2501.12521 | An Empirically-grounded tool for Automatic Prompt Linting and Repair: A
Case Study on Bias, Vulnerability, and Optimization in Developer Prompts | [
"cs.SE",
"cs.AI"
] | The tidal wave of advancements in Large Language Models (LLMs) has led to their swift integration into application-level logic. Many software systems now use prompts to interact with these black-box models, combining natural language with dynamic values interpolated at runtime, to perform tasks ranging from sentiment a... | {
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2501.12522 | Topology of Out-of-Distribution Examples in Deep Neural Networks | [
"cs.LG"
] | As deep neural networks (DNNs) become increasingly common, concerns about their robustness do as well. A longstanding problem for deployed DNNs is their behavior in the face of unfamiliar inputs; specifically, these models tend to be overconfident and incorrect when encountering out-of-distribution (OOD) examples. In t... | {
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2501.12523 | Federated Discrete Denoising Diffusion Model for Molecular Generation
with OpenFL | [
"cs.LG",
"cs.CR"
] | Generating unique molecules with biochemically desired properties to serve as viable drug candidates is a difficult task that requires specialized domain expertise. In recent years, diffusion models have shown promising results in accelerating the drug design process through AI-driven molecular generation. However, tra... | {
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2501.12524 | Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature
Extractor | [
"eess.IV",
"cs.AI",
"cs.CV"
] | With the advent of the COVID-19 pandemic, ultrasound imaging has emerged as a promising technique for COVID-19 detection, due to its non-invasive nature, affordability, and portability. In response, researchers have focused on developing AI-based scoring systems to provide real-time diagnostic support. However, the lim... | {
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2501.12528 | Improved Coded Caching Scheme for Multi-User Information Retrieval
System | [
"cs.IT",
"math.IT"
] | In this paper, we study the coded caching scheme for the $(L, K, M, N)$ multi-user information retrieval (MIR) system, which consists of a content library containing $N$ files, a base station (BS) with $L$ antennas that cannot access the library, and $K$ single-antenna users, each of which can cache at most $M$ files f... | {
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} |
2501.12535 | How Does the Spatial Distribution of Pre-training Data Affect Geospatial
Foundation Models? | [
"cs.LG",
"cs.CV"
] | Foundation models have made rapid advances in many domains including Earth observation, where Geospatial Foundation Models (GFMs) can help address global challenges such as climate change, agriculture, and disaster response. Previous work on GFMs focused on tailoring model architecture and pre-text tasks, and did not i... | {
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2501.12536 | Interaction Dataset of Autonomous Vehicles with Traffic Lights and Signs | [
"cs.RO",
"cs.AI"
] | This paper presents the development of a comprehensive dataset capturing interactions between Autonomous Vehicles (AVs) and traffic control devices, specifically traffic lights and stop signs. Derived from the Waymo Motion dataset, our work addresses a critical gap in the existing literature by providing real-world tra... | {
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2501.12537 | Enhancing Privacy in the Early Detection of Sexual Predators Through
Federated Learning and Differential Privacy | [
"cs.CL",
"cs.CY"
] | The increased screen time and isolation caused by the COVID-19 pandemic have led to a significant surge in cases of online grooming, which is the use of strategies by predators to lure children into sexual exploitation. Previous efforts to detect grooming in industry and academia have involved accessing and monitoring ... | {
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} |
2501.12538 | Academic Case Reports Lack Diversity: Assessing the Presence and
Diversity of Sociodemographic and Behavioral Factors related to Post COVID-19
Condition | [
"cs.CL",
"cs.AI"
] | Understanding the prevalence, disparities, and symptom variations of Post COVID-19 Condition (PCC) for vulnerable populations is crucial to improving care and addressing intersecting inequities. This study aims to develop a comprehensive framework for integrating social determinants of health (SDOH) into PCC research b... | {
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2501.12539 | Compositional Instruction Following with Language Models and
Reinforcement Learning | [
"cs.LG",
"cs.CL"
] | Combining reinforcement learning with language grounding is challenging as the agent needs to explore the environment while simultaneously learning multiple language-conditioned tasks. To address this, we introduce a novel method: the compositionally-enabled reinforcement learning language agent (CERLLA). Our method re... | {
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2501.12540 | Comparative Approaches to Sentiment Analysis Using Datasets in Major
European and Arabic Languages | [
"cs.CL"
] | This study explores transformer-based models such as BERT, mBERT, and XLM-R for multi-lingual sentiment analysis across diverse linguistic structures. Key contributions include the identification of XLM-R superior adaptability in morphologically complex languages, achieving accuracy levels above 88%. The work highlight... | {
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2501.12542 | Reinforcement Learning Constrained Beam Search for Parameter
Optimization of Paper Drying Under Flexible Constraints | [
"cs.LG",
"cs.AI",
"cs.SY",
"eess.SY"
] | Existing approaches to enforcing design constraints in Reinforcement Learning (RL) applications often rely on training-time penalties in the reward function or training/inference-time invalid action masking, but these methods either cannot be modified after training, or are limited in the types of constraints that can ... | {
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2501.12547 | Human-like conceptual representations emerge from language prediction | [
"cs.CL",
"cs.AI"
] | Recent advances in large language models (LLMs) provide a new opportunity to address the long-standing question of how concepts are represented and organized in the mind, which is central to unravelling the nature of human cognition. Here, we reframed the classic reverse dictionary task to simulate human concept infere... | {
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2501.12548 | Galaxy Codes: Advancing Achievability for Deterministic Identification
via Gaussian Channels | [
"cs.IT",
"math.IT"
] | Deterministic identification offers an efficient solution for scenarios where decoding entire messages is unnecessary. It is commonly used in alarm systems and control systems. A key advantage of this approach is that the capacity for deterministic identification in Gaussian channels with power constraints grows supere... | {
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2501.12553 | ViDDAR: Vision Language Model-Based Task-Detrimental Content Detection
for Augmented Reality | [
"cs.CV"
] | In Augmented Reality (AR), virtual content enhances user experience by providing additional information. However, improperly positioned or designed virtual content can be detrimental to task performance, as it can impair users' ability to accurately interpret real-world information. In this paper we examine two types o... | {
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2501.12554 | Generalization Performance of Hypergraph Neural Networks | [
"cs.LG"
] | Hypergraph neural networks have been promising tools for handling learning tasks involving higher-order data, with notable applications in web graphs, such as modeling multi-way hyperlink structures and complex user interactions. Yet, their generalization abilities in theory are less clear to us. In this paper, we seek... | {
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2501.12557 | Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at
CHI through a Systematic Literature Review | [
"cs.HC",
"cs.AI",
"cs.CL",
"cs.CY"
] | Large language models (LLMs) have been positioned to revolutionize HCI, by reshaping not only the interfaces, design patterns, and sociotechnical systems that we study, but also the research practices we use. To-date, however, there has been little understanding of LLMs' uptake in HCI. We address this gap via a systema... | {
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} |
2501.12558 | Structural and mechanical properties of W-Cu compounds characterized by
a neural-network-based potential | [
"cond-mat.mtrl-sci",
"cs.LG"
] | Tungsten-copper (W-Cu) compounds are widely utilized in various industrial fields due to their exceptional mechanical properties. In this study, we have developed a neural-network-based deep potential (DP) model that covers a wide range of temperatures, ranging from 0 to 3,000 K, and pressures, varying from 0 to 10 GPa... | {
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2501.12564 | Energy Landscape Shaping for Robust Control of Atoms in Optical Lattices | [
"quant-ph",
"cs.SY",
"eess.SY"
] | Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy lan... | {
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2501.12570 | O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning | [
"cs.CL"
] | Recently, long-thought reasoning LLMs, such as OpenAI's O1, adopt extended reasoning processes similar to how humans ponder over complex problems. This reasoning paradigm significantly enhances the model's problem-solving abilities and has achieved promising results. However, long-thought reasoning process leads to a s... | {
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2501.12571 | Exploring Unknown Social Networks for Discovering Hidden Nodes | [
"cs.SI",
"cs.CY"
] | In this paper, we address the challenge of discovering hidden nodes in unknown social networks, formulating three types of hidden-node discovery problems, namely, Sybil-node discovery, peripheral-node discovery, and influencer discovery. We tackle these problems by employing a graph exploration framework grounded in ma... | {
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2501.12573 | Leveraging LLMs to Create a Haptic Devices' Recommendation System | [
"cs.MM",
"cs.AI",
"cs.HC",
"cs.SY",
"eess.SY"
] | Haptic technology has seen significant growth, yet a lack of awareness of existing haptic device design knowledge hinders development. This paper addresses these limitations by leveraging advancements in Large Language Models (LLMs) to develop a haptic agent, focusing specifically on Grounded Force Feedback (GFF) devic... | {
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2501.12582 | Ultralow-dimensionality reduction for identifying critical transitions
by spatial-temporal PCA | [
"stat.ML",
"cs.LG"
] | Discovering dominant patterns and exploring dynamic behaviors especially critical state transitions and tipping points in high-dimensional time-series data are challenging tasks in study of real-world complex systems, which demand interpretable data representations to facilitate comprehension of both spatial and tempor... | {
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2501.12583 | Chasing price drains liquidity | [
"cs.CE"
] | Assuming that the price in a Uniswap v3 style Automated Market Maker (AMM) follows a Geometric Brownian Motion (GBM), we prove that the strategy that adjusts the position of liquidity to track the current price leads to a deterministic and exponentially fast decay of liquidity. Next, assuming that there is a Centralize... | {
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2501.12584 | Entropy Polarization-Based Data Compression Without Frozen Set
Construction | [
"cs.IT",
"eess.SP",
"math.IT"
] | Classical source polar codes require the construction of frozen sets for given sources. While this scheme offers excellent theoretical performance, it faces challenges in practical data compression systems, including sensitivity to the accuracy and computational complexity of the construction algorithm. In this letter,... | {
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2501.12587 | How Collective Intelligence Emerges in a Crowd of People Through Learned
Division of Labor: A Case Study | [
"cs.MA"
] | This paper investigates the factors fostering collective intelligence (CI) through a case study of *LinYi's Experiment, where over 2000 human players collectively controll an avatar car. By conducting theoretical analysis and replicating observed behaviors through numerical simulations, we demonstrate how self-organize... | {
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2501.12588 | Fundamental Limits of Non-Adaptive Group Testing with Markovian
Correlation | [
"cs.IT",
"math.IT"
] | We study a correlated group testing model where items are infected according to a Markov chain, which creates bursty binfection patterns. Focusing on a very sparse infections regime, we propose a non adaptive testing strategy with an efficient decoding scheme that is nearly optimal. Specifically, it achieves asymptotic... | {
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2501.12592 | FedGrAINS: Personalized SubGraph Federated Learning with Adaptive
Neighbor Sampling | [
"cs.LG",
"cs.AI",
"cs.DC",
"cs.IR"
] | Graphs are crucial for modeling relational and biological data. As datasets grow larger in real-world scenarios, the risk of exposing sensitive information increases, making privacy-preserving training methods like federated learning (FL) essential to ensure data security and compliance with privacy regulations. Recent... | {
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2501.12594 | A 3-Step Optimization Framework with Hybrid Models for a Humanoid
Robot's Jump Motion | [
"cs.RO"
] | High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper proposes a 3-step trajectory optimization framework for generating a jump motion for a ... | {
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2501.12595 | A Unified Invariant Learning Framework for Graph Classification | [
"cs.LG",
"cs.AI"
] | Invariant learning demonstrates substantial potential for enhancing the generalization of graph neural networks (GNNs) with out-of-distribution (OOD) data. It aims to recognize stable features in graph data for classification, based on the premise that these features causally determine the target label, and their influ... | {
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2501.12596 | Adapting OpenAI's CLIP Model for Few-Shot Image Inspection in
Manufacturing Quality Control: An Expository Case Study with Multiple
Application Examples | [
"cs.CV",
"stat.AP",
"stat.OT"
] | This expository paper introduces a simplified approach to image-based quality inspection in manufacturing using OpenAI's CLIP (Contrastive Language-Image Pretraining) model adapted for few-shot learning. While CLIP has demonstrated impressive capabilities in general computer vision tasks, its direct application to manu... | {
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2501.12597 | Multi-Instance Partial-Label Learning with Margin Adjustment | [
"cs.LG"
] | Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalizati... | {
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2501.12598 | On Accelerating Deep Neural Network Mutation Analysis by Neuron and
Mutant Clustering | [
"cs.SE",
"cs.LG",
"cs.NE"
] | Mutation analysis of deep neural networks (DNNs) is a promising method for effective evaluation of test data quality and model robustness, but it can be computationally expensive, especially for large models. To alleviate this, we present DEEPMAACC, a technique and a tool that speeds up DNN mutation analysis through ne... | {
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2501.12599 | Kimi k1.5: Scaling Reinforcement Learning with LLMs | [
"cs.AI",
"cs.LG"
] | Language model pretraining with next token prediction has proved effective for scaling compute but is limited to the amount of available training data. Scaling reinforcement learning (RL) unlocks a new axis for the continued improvement of artificial intelligence, with the promise that large language models (LLMs) can ... | {
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2501.12602 | BLR-MoE: Boosted Language-Routing Mixture of Experts for Domain-Robust
Multilingual E2E ASR | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Recently, the Mixture of Expert (MoE) architecture, such as LR-MoE, is often used to alleviate the impact of language confusion on the multilingual ASR (MASR) task. However, it still faces language confusion issues, especially in mismatched domain scenarios. In this paper, we decouple language confusion in LR-MoE into ... | {
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2501.12604 | Image Motion Blur Removal in the Temporal Dimension with Video Diffusion
Models | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Most motion deblurring algorithms rely on spatial-domain convolution models, which struggle with the complex, non-linear blur arising from camera shake and object motion. In contrast, we propose a novel single-image deblurring approach that treats motion blur as a temporal averaging phenomenon. Our core innovation lies... | {
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2501.12607 | Low-Dimensional Representation-Driven TSK Fuzzy System for Feature
Selection | [
"cs.LG"
] | Feature selection can select important features to address dimensional curses. Subspace learning, a widely used dimensionality reduction method, can project the original data into a low-dimensional space. However, the low-dimensional representation is often transformed back into the original space, resulting in informa... | {
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2501.12610 | Exploring Wikipedia Gender Diversity Over Time $\unicode{x2013}$ The
Wikipedia Gender Dashboard (WGD) | [
"cs.CY",
"cs.IR"
] | The Wikipedia editors' community has been actively pursuing the intent of achieving gender equality. To that end, it is important to explore the historical evolution of underlying gender disparities in Wikipedia articles. This paper presents the Wikipedia Gender Dashboard (WGD), a tool designed to enable the interactio... | {
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2501.12612 | T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in
Image Generation | [
"cs.CL",
"cs.CR"
] | Text-to-image (T2I) models have rapidly advanced, enabling the generation of high-quality images from text prompts across various domains. However, these models present notable safety concerns, including the risk of generating harmful, biased, or private content. Current research on assessing T2I safety remains in its ... | {
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2501.12615 | GATE: Adaptive Learning with Working Memory by Information Gating in
Multi-lamellar Hippocampal Formation | [
"q-bio.NC",
"cs.AI"
] | Hippocampal formation (HF) can rapidly adapt to varied environments and build flexible working memory (WM). To mirror the HF's mechanism on generalization and WM, we propose a model named Generalization and Associative Temporary Encoding (GATE), which deploys a 3-D multi-lamellar dorsoventral (DV) architecture, and lea... | {
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2501.12617 | Deep Learning-Based Identification of Inconsistent Method Names: How Far
Are We? | [
"cs.SE",
"cs.AI"
] | Concise and meaningful method names are crucial for program comprehension and maintenance. However, method names may become inconsistent with their corresponding implementations, causing confusion and errors. Several deep learning (DL)-based approaches have been proposed to identify such inconsistencies, with initial e... | {
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2501.12619 | Quantification of Large Language Model Distillation | [
"cs.CL"
] | Model distillation is a fundamental technique in building large language models (LLMs), transferring knowledge from a teacher model to a student model. However, distillation can lead to model homogenization, reducing diversity among models and impairing their ability to robustly handle complex or novel tasks. These lim... | {
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2501.12620 | Adaptive Data Exploitation in Deep Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | We introduce ADEPT: Adaptive Data ExPloiTation, a simple yet powerful framework to enhance the **data efficiency** and **generalization** in deep reinforcement learning (RL). Specifically, ADEPT adaptively manages the use of sampled data across different learning stages via multi-armed bandit (MAB) algorithms, optimizi... | {
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2501.12622 | Towards Robust Multi-tab Website Fingerprinting | [
"cs.CR",
"cs.AI"
] | Website fingerprinting enables an eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting (WF) attacks have demonstrated effectiveness even against Tor-protected network traffic. However, existing WF attacks have critical limitations on accuratel... | {
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2501.12624 | Toward Model-centric Heterogeneous Federated Graph Learning: A
Knowledge-driven Approach | [
"cs.LG",
"cs.DC"
] | Federated graph learning (FGL) has emerged as a promising paradigm for collaborative machine learning, enabling multiple parties to jointly train models while preserving the privacy of raw graph data. However, existing FGL methods often overlook the model-centric heterogeneous FGL (MHtFGL) problem, which arises in real... | {
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2501.12626 | The Intrinsic State Variable in Fundamental Lemma and Its Use in
Stability Design for Data-based Control | [
"eess.SY",
"cs.SY",
"math.DS"
] | In the data-based setting, analysis and control design of dynamical systems using measured data are typically based on overlapping trajectory segments of the input and output variables. This could lead to complex designs because the system internal dynamics, which is typically reflected by the system state variable, is... | {
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2501.12627 | Deep Reinforcement Learning with Hybrid Intrinsic Reward Model | [
"cs.LG"
] | Intrinsic reward shaping has emerged as a prevalent approach to solving hard-exploration and sparse-rewards environments in reinforcement learning (RL). While single intrinsic rewards, such as curiosity-driven or novelty-based methods, have shown effectiveness, they often limit the diversity and efficiency of explorati... | {
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2501.12632 | TeD-Loc: Text Distillation for Weakly Supervised Object Localization | [
"cs.CV",
"cs.LG"
] | Weakly supervised object localization (WSOL) using classification models trained with only image-class labels remains an important challenge in computer vision. Given their reliance on classification objectives, traditional WSOL methods like class activation mapping focus on the most discriminative object parts, often ... | {
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2501.12633 | Inverse Reinforcement Learning with Switching Rewards and History
Dependency for Characterizing Animal Behaviors | [
"cs.LG",
"cs.AI"
] | Traditional approaches to studying decision-making in neuroscience focus on simplified behavioral tasks where animals perform repetitive, stereotyped actions to receive explicit rewards. While informative, these methods constrain our understanding of decision-making to short timescale behaviors driven by explicit goals... | {
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2501.12635 | Multiple Queries with Multiple Keys: A Precise Prompt Matching Paradigm
for Prompt-based Continual Learning | [
"cs.CV"
] | Continual learning requires machine learning models to continuously acquire new knowledge in dynamic environments while avoiding the forgetting of previous knowledge. Prompt-based continual learning methods effectively address the issue of catastrophic forgetting through prompt expansion and selection. However, existin... | {
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2501.12637 | DWTNeRF: Boosting Few-shot Neural Radiance Fields via Discrete Wavelet
Transform | [
"cs.CV"
] | Neural Radiance Fields (NeRF) has achieved superior performance in novel view synthesis and 3D scene representation, but its practical applications are hindered by slow convergence and reliance on dense training views. To this end, we present DWTNeRF, a unified framework based on Instant-NGP's fast-training hash encodi... | {
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} |
2501.12640 | Dynamics of Toxicity in Political Podcasts | [
"cs.CL",
"cs.AI"
] | Toxicity in digital media poses significant challenges, yet little attention has been given to its dynamics within the rapidly growing medium of podcasts. This paper addresses this gap by analyzing political podcast data to study the emergence and propagation of toxicity, focusing on conversation chains-structured repl... | {
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} |
2501.12644 | Current Opinions on Memristor-Accelerated Machine Learning Hardware | [
"cs.ET",
"cs.AR",
"cs.LG",
"eess.SP",
"physics.app-ph"
] | The unprecedented advancement of artificial intelligence has placed immense demands on computing hardware, but traditional silicon-based semiconductor technologies are approaching their physical and economic limit, prompting the exploration of novel computing paradigms. Memristor offers a promising solution, enabling i... | {
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} |
2501.12651 | The potential -- and the pitfalls -- of using pre-trained language
models as cognitive science theories | [
"cs.CL",
"cs.AI"
] | Many studies have evaluated the cognitive alignment of Pre-trained Language Models (PLMs), i.e., their correspondence to adult performance across a range of cognitive domains. Recently, the focus has expanded to the developmental alignment of these models: identifying phases during training where improvements in model ... | {
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} |
2501.12654 | AnyNav: Visual Neuro-Symbolic Friction Learning for Off-road Navigation | [
"cs.RO"
] | Off-road navigation is essential for a wide range of applications in field robotics such as planetary exploration and disaster response. However, it remains an unresolved challenge due to the unstructured environments and inherent complexity of terrain-vehicle interactions. Traditional physics-based methods struggle to... | {
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} |
2501.12656 | PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced
C-V2X | [
"cs.NI",
"cs.LG"
] | On-ramp merging presents a critical challenge in autonomous driving, as vehicles from merging lanes need to dynamically adjust their positions and speeds while monitoring traffic on the main road to prevent collisions. To address this challenge, we propose a novel merging control scheme based on reinforcement learning,... | {
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} |
2501.12660 | Extracting General-use Transformers for Low-resource Languages via
Knowledge Distillation | [
"cs.CL"
] | In this paper, we propose the use of simple knowledge distillation to produce smaller and more efficient single-language transformers from Massively Multilingual Transformers (MMTs) to alleviate tradeoffs associated with the use of such in low-resource settings. Using Tagalog as a case study, we show that these smaller... | {
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} |
2501.12666 | Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization | [
"cs.LG",
"cs.CV"
] | Sharpness-Aware Minimization (SAM) has attracted significant attention for its effectiveness in improving generalization across various tasks. However, its underlying principles remain poorly understood. In this work, we analyze SAM's training dynamics using the maximum eigenvalue of the Hessian as a measure of sharpne... | {
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} |
2501.12667 | Sequential Change Point Detection via Denoising Score Matching | [
"stat.ML",
"cs.LG"
] | Sequential change-point detection plays a critical role in numerous real-world applications, where timely identification of distributional shifts can greatly mitigate adverse outcomes. Classical methods commonly rely on parametric density assumptions of pre- and post-change distributions, limiting their effectiveness f... | {
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} |
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