id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.08870 | When and why randomised exploration works (in linear bandits) | [
"cs.LG",
"stat.ML"
] | We provide an approach for the analysis of randomised exploration algorithms like Thompson sampling that does not rely on forced optimism or posterior inflation. With this, we demonstrate that in the $d$-dimensional linear bandit setting, when the action space is smooth and strongly convex, randomised exploration algor... | {
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2502.08873 | Robust Graph-Based Semi-Supervised Learning via $p$-Conductances | [
"cs.LG",
"cs.DM",
"math.OC"
] | We study the problem of semi-supervised learning on graphs in the regime where data labels are scarce or possibly corrupted. We propose an approach called $p$-conductance learning that generalizes the $p$-Laplace and Poisson learning methods by introducing an objective reminiscent of $p$-Laplacian regularization and an... | {
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2502.08874 | Data Sensor Fusion In Digital Twin Technology For Enhanced Capabilities
In A Home Environment | [
"cs.AI",
"cs.LG",
"eess.SP"
] | This paper investigates the integration of data sensor fusion in digital twin technology to bolster home environment capabilities, particularly in the context of challenges brought on by the coronavirus pandemic and its economic effects. The study underscores the crucial role of digital transformation in not just adapt... | {
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2502.08881 | WENDy for Nonlinear-in-Parameters ODEs | [
"cs.LG",
"stat.ME",
"stat.ML"
] | The Weak-form Estimation of Non-linear Dynamics (WENDy) algorithm is extended to accommodate systems of ordinary differential equations that are nonlinear-in-parameters. The extension rests on derived analytic expressions for a likelihood function, its gradient and its Hessian matrix. WENDy makes use of these to approx... | {
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2502.08882 | 2D Integrated Bayesian Tomography of Plasma Electron Density Profile for
HL-3 Based on Gaussian Process | [
"cs.LG"
] | This paper introduces an integrated Bayesian model that combines line integral measurements and point values using Gaussian Process (GP). The proposed method leverages Gaussian Process Regression (GPR) to incorporate point values into 2D profiles and employs coordinate mapping to integrate magnetic flux information for... | {
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2502.08884 | ShapeLib: designing a library of procedural 3D shape abstractions with
Large Language Models | [
"cs.CV",
"cs.AI",
"cs.GR"
] | Procedural representations are desirable, versatile, and popular shape encodings. Authoring them, either manually or using data-driven procedures, remains challenging, as a well-designed procedural representation should be compact, intuitive, and easy to manipulate. A long-standing problem in shape analysis studies how... | {
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2502.08886 | Generative AI for Internet of Things Security: Challenges and
Opportunities | [
"cs.CR",
"cs.AI"
] | As Generative AI (GenAI) continues to gain prominence and utility across various sectors, their integration into the realm of Internet of Things (IoT) security evolves rapidly. This work delves into an examination of the state-of-the-art literature and practical applications on how GenAI could improve and be applied in... | {
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2502.08888 | LLM-Enhanced Multiple Instance Learning for Joint Rumor and Stance
Detection with Social Context Information | [
"cs.CL"
] | The proliferation of misinformation, such as rumors on social media, has drawn significant attention, prompting various expressions of stance among users. Although rumor detection and stance detection are distinct tasks, they can complement each other. Rumors can be identified by cross-referencing stances in related po... | {
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2502.08889 | Linear-Time User-Level DP-SCO via Robust Statistics | [
"cs.LG",
"cs.CR",
"cs.DS",
"stat.ML"
] | User-level differentially private stochastic convex optimization (DP-SCO) has garnered significant attention due to the paramount importance of safeguarding user privacy in modern large-scale machine learning applications. Current methods, such as those based on differentially private stochastic gradient descent (DP-SG... | {
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2502.08896 | Communication is All You Need: Persuasion Dataset Construction via
Multi-LLM Communication | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have shown proficiency in generating persuasive dialogue, yet concerns about the fluency and sophistication of their outputs persist. This paper presents a multi-LLM communication framework designed to enhance the generation of persuasive data automatically. This framework facilitates the e... | {
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2502.08898 | Learning in Strategic Queuing Systems with Small Buffers | [
"cs.GT",
"cs.AI",
"cs.MA"
] | Routers in networking use simple learning algorithms to find the best way to deliver packets to their desired destination. This simple, myopic and distributed decision system makes large queuing systems simple to operate, but at the same time, the system needs more capacity than would be required if all traffic were ce... | {
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2502.08900 | Can Uniform Meaning Representation Help GPT-4 Translate from Indigenous
Languages? | [
"cs.CL"
] | While ChatGPT and GPT-based models are able to effectively perform many tasks without additional fine-tuning, they struggle with related to extremely low-resource languages and indigenous languages. Uniform Meaning Representation (UMR), a semantic representation designed to capture the meaning of texts in many language... | {
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2502.08902 | CoL3D: Collaborative Learning of Single-view Depth and Camera Intrinsics
for Metric 3D Shape Recovery | [
"cs.CV"
] | Recovering the metric 3D shape from a single image is particularly relevant for robotics and embodied intelligence applications, where accurate spatial understanding is crucial for navigation and interaction with environments. Usually, the mainstream approaches achieve it through monocular depth estimation. However, wi... | {
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2502.08903 | 3D-Grounded Vision-Language Framework for Robotic Task Planning:
Automated Prompt Synthesis and Supervised Reasoning | [
"cs.RO",
"cs.AI"
] | Vision-language models (VLMs) have achieved remarkable success in scene understanding and perception tasks, enabling robots to plan and execute actions adaptively in dynamic environments. However, most multimodal large language models lack robust 3D scene localization capabilities, limiting their effectiveness in fine-... | {
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2502.08904 | MIH-TCCT: Mitigating Inconsistent Hallucinations in LLMs via
Event-Driven Text-Code Cyclic Training | [
"cs.AI"
] | Recent methodologies utilizing synthetic datasets have aimed to address inconsistent hallucinations in large language models (LLMs); however,these approaches are primarily tailored to specific tasks, limiting their generalizability. Inspired by the strong performance of code-trained models in logic-intensive domains, w... | {
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2502.08905 | DiffoRA: Enabling Parameter-Efficient LLM Fine-Tuning via Differential
Low-Rank Matrix Adaptation | [
"cs.CV"
] | The Parameter-Efficient Fine-Tuning (PEFT) methods have been extensively researched for large language models in the downstream tasks. Among all the existing approaches, the Low-Rank Adaptation (LoRA) has gained popularity for its streamlined design by incorporating low-rank matrices into existing pre-trained models. T... | {
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2502.08908 | Reinforced Large Language Model is a formal theorem prover | [
"cs.AI"
] | To take advantage of Large Language Model in theorem formalization and proof, we propose a reinforcement learning framework to iteratively optimize the pretrained LLM by rolling out next tactics and comparing them with the expected ones. The experiment results show that it helps to achieve a higher accuracy compared wi... | {
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2502.08909 | Towards Automated Fact-Checking of Real-World Claims: Exploring Task
Formulation and Assessment with LLMs | [
"cs.CL",
"cs.AI"
] | Fact-checking is necessary to address the increasing volume of misinformation. Traditional fact-checking relies on manual analysis to verify claims, but it is slow and resource-intensive. This study establishes baseline comparisons for Automated Fact-Checking (AFC) using Large Language Models (LLMs) across multiple lab... | {
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2502.08910 | InfiniteHiP: Extending Language Model Context Up to 3 Million Tokens on
a Single GPU | [
"cs.CL",
"cs.LG"
] | In modern large language models (LLMs), handling very long context lengths presents significant challenges as it causes slower inference speeds and increased memory costs. Additionally, most existing pre-trained LLMs fail to generalize beyond their original training sequence lengths. To enable efficient and practical l... | {
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2502.08914 | Diffusion Models Through a Global Lens: Are They Culturally Inclusive? | [
"cs.CV",
"cs.AI"
] | Text-to-image diffusion models have recently enabled the creation of visually compelling, detailed images from textual prompts. However, their ability to accurately represent various cultural nuances remains an open question. In our work, we introduce CultDiff benchmark, evaluating state-of-the-art diffusion models whe... | {
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2502.08916 | PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic
Decision-Making Applied to Histopathology | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.MA"
] | Diagnosing diseases through histopathology whole slide images (WSIs) is fundamental in modern pathology but is challenged by the gigapixel scale and complexity of WSIs. Trained histopathologists overcome this challenge by navigating the WSI, looking for relevant patches, taking notes, and compiling them to produce a fi... | {
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2502.08918 | CLEAR: Cluster-based Prompt Learning on Heterogeneous Graphs | [
"cs.LG"
] | Prompt learning has attracted increasing attention in the graph domain as a means to bridge the gap between pretext and downstream tasks. Existing studies on heterogeneous graph prompting typically use feature prompts to modify node features for specific downstream tasks, which do not concern the structure of heterogen... | {
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2502.08920 | Exploring Emotion-Sensitive LLM-Based Conversational AI | [
"cs.HC",
"cs.AI"
] | Conversational AI chatbots have become increasingly common within the customer service industry. Despite improvements in their emotional development, they often lack the authenticity of real customer service interactions or the competence of service providers. By comparing emotion-sensitive and emotion-insensitive LLM-... | {
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2502.08921 | Detecting Malicious Concepts Without Image Generation in AIGC | [
"cs.CR",
"cs.CV"
] | The task of text-to-image generation has achieved tremendous success in practice, with emerging concept generation models capable of producing highly personalized and customized content. Fervor for concept generation is increasing rapidly among users, and platforms for concept sharing have sprung up. The concept owners... | {
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2502.08922 | Self-Consistency of the Internal Reward Models Improves Self-Rewarding
Language Models | [
"cs.AI"
] | Aligning Large Language Models (LLMs) with human preferences is crucial for their deployment in real-world applications. Recent advancements in Self-Rewarding Language Models suggest that an LLM can use its internal reward models (such as LLM-as-a-Judge) \cite{yuanself} to generate preference data, improving alignment ... | {
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2502.08923 | CopySpec: Accelerating LLMs with Speculative Copy-and-Paste Without
Compromising Quality | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We introduce CopySpec, an innovative technique designed to tackle the inefficiencies LLMs face when generating responses that closely resemble previous outputs. CopySpec identifies repeated sequences in the model's chat history and speculates that the same tokens will follow, enabling seamless copying without compromis... | {
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2502.08924 | Escaping Collapse: The Strength of Weak Data for Large Language Model
Training | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Synthetically-generated data plays an increasingly larger role in training large language models. However, while synthetic data has been found to be useful, studies have also shown that without proper curation it can cause LLM performance to plateau, or even "collapse", after many training iterations. In this paper, we... | {
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2502.08927 | Dynamic watermarks in images generated by diffusion models | [
"cs.CV"
] | High-fidelity text-to-image diffusion models have revolutionized visual content generation, but their widespread use raises significant ethical concerns, including intellectual property protection and the misuse of synthetic media. To address these challenges, we propose a novel multi-stage watermarking framework for d... | {
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2502.08932 | On the Promise for Assurance of Differentiable Neurosymbolic Reasoning
Paradigms | [
"cs.AI",
"cs.CV"
] | To create usable and deployable Artificial Intelligence (AI) systems, there requires a level of assurance in performance under many different conditions. Many times, deployed machine learning systems will require more classic logic and reasoning performed through neurosymbolic programs jointly with artificial neural ne... | {
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2502.08933 | AutoLike: Auditing Social Media Recommendations through User
Interactions | [
"cs.LG"
] | Modern social media platforms, such as TikTok, Facebook, and YouTube, rely on recommendation systems to personalize content for users based on user interactions with endless streams of content, such as "For You" pages. However, these complex algorithms can inadvertently deliver problematic content related to self-harm,... | {
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2502.08938 | Reevaluating Policy Gradient Methods for Imperfect-Information Games | [
"cs.LG"
] | In the past decade, motivated by the putative failure of naive self-play deep reinforcement learning (DRL) in adversarial imperfect-information games, researchers have developed numerous DRL algorithms based on fictitious play (FP), double oracle (DO), and counterfactual regret minimization (CFR). In light of recent re... | {
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2502.08939 | TokenSynth: A Token-based Neural Synthesizer for Instrument Cloning and
Text-to-Instrument | [
"cs.SD",
"cs.AI"
] | Recent advancements in neural audio codecs have enabled the use of tokenized audio representations in various audio generation tasks, such as text-to-speech, text-to-audio, and text-to-music generation. Leveraging this approach, we propose TokenSynth, a novel neural synthesizer that utilizes a decoder-only transformer ... | {
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2502.08940 | Towards Understanding Why Data Augmentation Improves Generalization | [
"cs.CV",
"cs.LG",
"stat.ML"
] | Data augmentation is a cornerstone technique in deep learning, widely used to improve model generalization. Traditional methods like random cropping and color jittering, as well as advanced techniques such as CutOut, Mixup, and CutMix, have achieved notable success across various domains. However, the mechanisms by whi... | {
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2502.08941 | Analysis of Off-Policy $n$-Step TD-Learning with Linear Function
Approximation | [
"cs.LG",
"cs.AI"
] | This paper analyzes multi-step temporal difference (TD)-learning algorithms within the ``deadly triad'' scenario, characterized by linear function approximation, off-policy learning, and bootstrapping. In particular, we prove that $n$-step TD-learning algorithms converge to a solution as the sampling horizon $n$ increa... | {
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2502.08942 | Language in the Flow of Time: Time-Series-Paired Texts Weaved into a
Unified Temporal Narrative | [
"cs.LG",
"cs.AI"
] | While many advances in time series models focus exclusively on numerical data, research on multimodal time series, particularly those involving contextual textual information commonly encountered in real-world scenarios, remains in its infancy. Consequently, effectively integrating the text modality remains challenging... | {
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2502.08943 | Beyond the Singular: The Essential Role of Multiple Generations in
Effective Benchmark Evaluation and Analysis | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models (LLMs) have demonstrated significant utilities in real-world applications, exhibiting impressive capabilities in natural language processing and understanding. Benchmark evaluations are crucial for assessing the capabilities of LLMs as they can provide a comprehensive assessment of their strengths... | {
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2502.08946 | The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of
Physical Concept Understanding | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.LG"
] | In a systematic way, we investigate a widely asked question: Do LLMs really understand what they say?, which relates to the more familiar term Stochastic Parrot. To this end, we propose a summative assessment over a carefully designed physical concept understanding task, PhysiCo. Our task alleviates the memorization is... | {
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2502.08947 | Structured Convergence in Large Language Model Representations via
Hierarchical Latent Space Folding | [
"cs.CL"
] | Token representations in high-dimensional latent spaces often exhibit redundancy, limiting computational efficiency and reducing structural coherence across model layers. Hierarchical latent space folding introduces a structured transformation mechanism that enforces a multi-scale organization within learned embeddings... | {
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2502.08949 | Self-Supervised Graph Contrastive Pretraining for Device-level
Integrated Circuits | [
"cs.LG"
] | Self-supervised graph representation learning has driven significant advancements in domains such as social network analysis, molecular design, and electronics design automation (EDA). However, prior works in EDA have mainly focused on the representation of gate-level digital circuits, failing to capture analog and mix... | {
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2502.08950 | Single-Agent Planning in a Multi-Agent System: A Unified Framework for
Type-Based Planners | [
"cs.MA",
"cs.GT"
] | We consider a general problem where an agent is in a multi-agent environment and must plan for herself without any prior information about her opponents. At each moment, this pivotal agent is faced with a trade-off between exploiting her currently accumulated information about the other agents and exploring further to ... | {
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2502.08953 | Integrated Optimization and Game Theory Framework for Fair Cost
Allocation in Community Microgrids | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Fair cost allocation in community microgrids remains a significant challenge due to the complex interactions between multiple participants with varying load profiles, distributed energy resources, and storage systems. Traditional cost allocation methods often fail to adequately address the dynamic nature of participant... | {
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2502.08954 | Medicine on the Edge: Comparative Performance Analysis of On-Device LLMs
for Clinical Reasoning | [
"cs.CL"
] | The deployment of Large Language Models (LLM) on mobile devices offers significant potential for medical applications, enhancing privacy, security, and cost-efficiency by eliminating reliance on cloud-based services and keeping sensitive health data local. However, the performance and accuracy of on-device LLMs in real... | {
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2502.08957 | Training Trajectory Predictors Without Ground-Truth Data | [
"cs.RO"
] | This paper presents a framework capable of accurately and smoothly estimating position, heading, and velocity. Using this high-quality input, we propose a system based on Trajectron++, able to consistently generate precise trajectory predictions. Unlike conventional models that require ground-truth data for training, o... | {
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2502.08958 | Biologically Plausible Brain Graph Transformer | [
"cs.LG",
"cs.AI"
] | State-of-the-art brain graph analysis methods fail to fully encode the small-world architecture of brain graphs (accompanied by the presence of hubs and functional modules), and therefore lack biological plausibility to some extent. This limitation hinders their ability to accurately represent the brain's structural an... | {
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2502.08960 | A Comprehensive Survey on Imbalanced Data Learning | [
"cs.LG"
] | With the expansion of data availability, machine learning (ML) has achieved remarkable breakthroughs in both academia and industry. However, imbalanced data distributions are prevalent in various types of raw data and severely hinder the performance of ML by biasing the decision-making processes. To deepen the understa... | {
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2502.08963 | Modeling Time-evolving Causality over Data Streams | [
"cs.LG"
] | Given an extensive, semi-infinite collection of multivariate coevolving data sequences (e.g., sensor/web activity streams) whose observations influence each other, how can we discover the time-changing cause-and-effect relationships in co-evolving data streams? How efficiently can we reveal dynamical patterns that allo... | {
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2502.08966 | RTBAS: Defending LLM Agents Against Prompt Injection and Privacy Leakage | [
"cs.CR",
"cs.AI"
] | Tool-Based Agent Systems (TBAS) allow Language Models (LMs) to use external tools for tasks beyond their standalone capabilities, such as searching websites, booking flights, or making financial transactions. However, these tools greatly increase the risks of prompt injection attacks, where malicious content hijacks th... | {
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2502.08967 | Low Complexity Artificial Noise Aided Beam Focusing Design in Near-Field
Terahertz Communications | [
"cs.IT",
"math.IT"
] | In this paper, we develop a novel low-complexity artificial noise (AN) aided beam focusing scheme in a near-field terahertz wiretap communication system. In this system, the base station (BS) equipped with a large-scale array transmits signals to a legitimate user, while mitigating information leakage to an eavesdroppe... | {
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2502.08969 | SkyRover: A Modular Simulator for Cross-Domain Pathfinding | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.MA"
] | Unmanned Aerial Vehicles (UAVs) and Automated Guided Vehicles (AGVs) increasingly collaborate in logistics, surveillance, inspection tasks and etc. However, existing simulators often focus on a single domain, limiting cross-domain study. This paper presents the SkyRover, a modular simulator for UAV-AGV multi-agent path... | {
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2502.08972 | Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context
Learning | [
"cs.CL",
"cs.AI"
] | Language models are aligned to the collective voice of many, resulting in generic outputs that do not align with specific users' styles. In this work, we present Trial-Error-Explain In-Context Learning (TICL), a tuning-free method that personalizes language models for text generation tasks with fewer than 10 examples p... | {
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2502.08974 | Topo2Seq: Enhanced Topology Reasoning via Topology Sequence Learning | [
"cs.CV"
] | Extracting lane topology from perspective views (PV) is crucial for planning and control in autonomous driving. This approach extracts potential drivable trajectories for self-driving vehicles without relying on high-definition (HD) maps. However, the unordered nature and weak long-range perception of the DETR-like fra... | {
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2502.08975 | Small Molecule Drug Discovery Through Deep Learning:Progress,
Challenges, and Opportunities | [
"cs.LG",
"q-bio.BM"
] | Due to their excellent drug-like and pharmacokinetic properties, small molecule drugs are widely used to treat various diseases, making them a critical component of drug discovery. In recent years, with the rapid development of deep learning (DL) techniques, DL-based small molecule drug discovery methods have achieved ... | {
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2502.08977 | Text-driven 3D Human Generation via Contrastive Preference Optimization | [
"cs.CV"
] | Recent advances in Score Distillation Sampling (SDS) have improved 3D human generation from textual descriptions. However, existing methods still face challenges in accurately aligning 3D models with long and complex textual inputs. To address this challenge, we propose a novel framework that introduces contrastive pre... | {
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2502.08978 | What exactly has TabPFN learned to do? | [
"cs.LG",
"stat.ML"
] | TabPFN [Hollmann et al., 2023], a Transformer model pretrained to perform in-context learning on fresh tabular classification problems, was presented at the last ICLR conference. To better understand its behavior, we treat it as a black-box function approximator generator and observe its generated function approximatio... | {
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2502.08982 | Outback: Fast and Communication-efficient Index for Key-Value Store on
Disaggregated Memory | [
"cs.DB"
] | Disaggregated memory systems achieve resource utilization efficiency and system scalability by distributing computation and memory resources into distinct pools of nodes. RDMA is an attractive solution to support high-throughput communication between different disaggregated resource pools. However, existing RDMA soluti... | {
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2502.08985 | Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline
Multi-Agent Reinforcement Learning | [
"cs.LG",
"cs.AI",
"cs.MA"
] | As a data-driven approach, offline MARL learns superior policies solely from offline datasets, ideal for domains rich in historical data but with high interaction costs and risks. However, most existing methods are task-specific, requiring retraining for new tasks, leading to redundancy and inefficiency. To address thi... | {
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2502.08987 | Neural Force Field: Learning Generalized Physical Representation from a
Few Examples | [
"cs.LG",
"cs.AI"
] | Physical reasoning is a remarkable human ability that enables rapid learning and generalization from limited experience. Current AI models, despite extensive training, still struggle to achieve similar generalization, especially in Out-of-distribution (OOD) settings. This limitation stems from their inability to abstra... | {
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2502.08988 | Latents of latents to delineate pixels: hybrid Matryoshka
autoencoder-to-U-Net pairing for segmenting large medical images in GPU-poor
and low-data regimes | [
"cs.CV"
] | Medical images are often high-resolution and lose important detail if downsampled, making pixel-level methods such as semantic segmentation much less efficient if performed on a low-dimensional image. We propose a low-rank Matryoshka projection and a hybrid segmenting architecture that preserves important information w... | {
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2502.08989 | RLSA-PFL: Robust Lightweight Secure Aggregation with Model Inconsistency
Detection in Privacy-Preserving Federated Learning | [
"cs.CR",
"cs.AI"
] | Federated Learning (FL) allows users to collaboratively train a global machine learning model by sharing local model only, without exposing their private data to a central server. This distributed learning is particularly appealing in scenarios where data privacy is crucial, and it has garnered substantial attention fr... | {
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2502.08991 | Task Generalization With AutoRegressive Compositional Structure: Can
Learning From $\d$ Tasks Generalize to $\d^{T}$ Tasks? | [
"cs.LG",
"stat.ML"
] | Large language models (LLMs) exhibit remarkable task generalization, solving tasks they were never explicitly trained on with only a few demonstrations. This raises a fundamental question: When can learning from a small set of tasks generalize to a large task family? In this paper, we investigate task generalization th... | {
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2502.08993 | Off-Policy Evaluation for Recommendations with Missing-Not-At-Random
Rewards | [
"stat.ML",
"cs.LG"
] | Unbiased recommender learning (URL) and off-policy evaluation/learning (OPE/L) techniques are effective in addressing the data bias caused by display position and logging policies, thereby consistently improving the performance of recommendations. However, when both bias exits in the logged data, these estimators may s... | {
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2502.08995 | PixLift: Accelerating Web Browsing via AI Upscaling | [
"cs.PF",
"cs.AI"
] | Accessing the internet in regions with expensive data plans and limited connectivity poses significant challenges, restricting information access and economic growth. Images, as a major contributor to webpage sizes, exacerbate this issue, despite advances in compression formats like WebP and AVIF. The continued growth ... | {
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2502.08996 | Masked Modulation: High-Throughput Half-Duplex ISAC Transmission
Waveform Design | [
"cs.IT",
"math.IT"
] | Integrated sensing and communication (ISAC) enables numerous innovative wireless applications. Communication-centric design is a practical choice for the construction of the sixth generation (6G) ISAC networks. Continuous-wave-based ISAC systems, with orthogonal frequency-division multiplexing (OFDM) being a representa... | {
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2502.08997 | Hierarchical Vision Transformer with Prototypes for Interpretable
Medical Image Classification | [
"cs.CV"
] | Explainability is a highly demanded requirement for applications in high-risk areas such as medicine. Vision Transformers have mainly been limited to attention extraction to provide insight into the model's reasoning. Our approach combines the high performance of Vision Transformers with the introduction of new explain... | {
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2502.09000 | Residual Transformer Fusion Network for Salt and Pepper Image Denoising | [
"cs.CV",
"cs.LG"
] | Convolutional Neural Network (CNN) has been widely used in unstructured datasets, one of which is image denoising. Image denoising is a noisy image reconstruction process that aims to reduce additional noise that occurs from the noisy image with various strategies. Image denoising has a problem, namely that some image ... | {
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2502.09001 | Privacy-Preserving Hybrid Ensemble Model for Network Anomaly Detection:
Balancing Security and Data Protection | [
"cs.LG"
] | Privacy-preserving network anomaly detection has become an essential area of research due to growing concerns over the protection of sensitive data. Traditional anomaly detection models often prioritize accuracy while neglecting the critical aspect of privacy. In this work, we propose a hybrid ensemble model that incor... | {
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2502.09002 | End-to-End triplet loss based fine-tuning for network embedding in
effective PII detection | [
"cs.LG"
] | There are many approaches in mobile data ecosystem that inspect network traffic generated by applications running on user's device to detect personal data exfiltration from the user's device. State-of-the-art methods rely on features extracted from HTTP requests and in this context, machine learning involves training c... | {
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2502.09003 | RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach
for Large Language Models | [
"cs.LG",
"cs.AI"
] | Supervised fine-tuning is a standard method for adapting pre-trained large language models (LLMs) to downstream tasks. Quantization has been recently studied as a post-training technique for efficient LLM deployment. To obtain quantized fine-tuned LLMs, conventional pipelines would first fine-tune the pre-trained model... | {
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2502.09004 | Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content
in Mainstream US News Media through the Lens of Hope Speech | [
"cs.CL",
"cs.CY",
"cs.LG"
] | This paper makes three contributions. First, via a substantial corpus of 1,419,047 comments posted on 3,161 YouTube news videos of major US cable news outlets, we analyze how users engage with LGBTQ+ news content. Our analyses focus both on positive and negative content. In particular, we construct a fine-grained hope ... | {
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2502.09010 | Data-Driven Discovery of Population Balance Equations for the
Particulate Sciences | [
"cs.CE"
] | Understanding the behavior of particles in a dispersed phase system via population balances holds fundamental importance in studies of particulate sciences across various fields. Particle behavior, however, is sophisticated as a single particle can undergo internal property changes (e.g., size, cell age, and energy con... | {
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2502.09017 | Diversity Enhances an LLM's Performance in RAG and Long-context Task | [
"cs.CL",
"cs.LG"
] | The rapid advancements in large language models (LLMs) have highlighted the challenge of context window limitations, primarily due to the quadratic time complexity of the self-attention mechanism (\(O(N^2)\), where \(N\) denotes the context window length). This constraint impacts tasks such as retrieval-augmented gener... | {
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2502.09018 | Zero-shot Concept Bottleneck Models | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Concept bottleneck models (CBMs) are inherently interpretable and intervenable neural network models, which explain their final label prediction by the intermediate prediction of high-level semantic concepts. However, they require target task training to learn input-to-concept and concept-to-label mappings, incurring t... | {
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2502.09020 | EventSTR: A Benchmark Dataset and Baselines for Event Stream based Scene
Text Recognition | [
"cs.CV",
"cs.AI"
] | Mainstream Scene Text Recognition (STR) algorithms are developed based on RGB cameras which are sensitive to challenging factors such as low illumination, motion blur, and cluttered backgrounds. In this paper, we propose to recognize the scene text using bio-inspired event cameras by collecting and annotating a large-s... | {
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2502.09022 | Mechanistic Unveiling of Transformer Circuits: Self-Influence as a Key
to Model Reasoning | [
"cs.AI"
] | Transformer-based language models have achieved significant success; however, their internal mechanisms remain largely opaque due to the complexity of non-linear interactions and high-dimensional operations. While previous studies have demonstrated that these models implicitly embed reasoning trees, humans typically em... | {
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2502.09026 | Billet Number Recognition Based on Test-Time Adaptation | [
"cs.CV"
] | During the steel billet production process, it is essential to recognize machine-printed or manually written billet numbers on moving billets in real-time. To address the issue of low recognition accuracy for existing scene text recognition methods, caused by factors such as image distortions and distribution differenc... | {
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2502.09027 | A Contextual-Aware Position Encoding for Sequential Recommendation | [
"cs.IR"
] | Sequential recommendation (SR), which encodes user activity to predict the next action, has emerged as a widely adopted strategy in developing commercial personalized recommendation systems. A critical component of modern SR models is the attention mechanism, which synthesizes users' historical activities. This mechani... | {
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2502.09029 | MTDP: Modulated Transformer Diffusion Policy Model | [
"cs.RO"
] | Recent research on robot manipulation based on Behavior Cloning (BC) has made significant progress. By combining diffusion models with BC, diffusion policiy has been proposed, enabling robots to quickly learn manipulation tasks with high success rates. However, integrating diffusion policy with high-capacity Transforme... | {
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2502.09038 | AoI-Sensitive Data Forwarding with Distributed Beamforming in
UAV-Assisted IoT | [
"cs.AI"
] | This paper proposes a UAV-assisted forwarding system based on distributed beamforming to enhance age of information (AoI) in Internet of Things (IoT). Specifically, UAVs collect and relay data between sensor nodes (SNs) and the remote base station (BS). However, flight delays increase the AoI and degrade the network pe... | {
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2502.09039 | Large Images are Gaussians: High-Quality Large Image Representation with
Levels of 2D Gaussian Splatting | [
"cs.CV",
"cs.AI"
] | While Implicit Neural Representations (INRs) have demonstrated significant success in image representation, they are often hindered by large training memory and slow decoding speed. Recently, Gaussian Splatting (GS) has emerged as a promising solution in 3D reconstruction due to its high-quality novel view synthesis an... | {
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2502.09042 | Typhoon T1: An Open Thai Reasoning Model | [
"cs.CL",
"cs.AI"
] | This paper introduces Typhoon T1, an open effort to develop an open Thai reasoning model. A reasoning model is a relatively new type of generative model built on top of large language models (LLMs). A reasoning model generates a long chain of thought before arriving at a final answer, an approach found to improve perfo... | {
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2502.09045 | Evolution of Data-driven Single- and Multi-Hazard Susceptibility Mapping
and Emergence of Deep Learning Methods | [
"cs.CV"
] | Data-driven susceptibility mapping of natural hazards has harnessed the advances in classification methods used on heterogeneous sources represented as raster images. Susceptibility mapping is an important step towards risk assessment for any natural hazard. Increasingly, multiple hazards co-occur spatially, temporally... | {
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2502.09046 | Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate
Multi-Criteria Recommendation | [
"cs.IR",
"cs.AI",
"cs.IT",
"cs.LG",
"cs.SI",
"math.IT"
] | Multi-criteria (MC) recommender systems, which utilize MC rating information for recommendation, are increasingly widespread in various e-commerce domains. However, the MC recommendation using training-based collaborative filtering, requiring consideration of multiple ratings compared to single-criterion counterparts, ... | {
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2502.09047 | Optimal Algorithms in Linear Regression under Covariate Shift: On the
Importance of Precondition | [
"stat.ML",
"cs.LG"
] | A common pursuit in modern statistical learning is to attain satisfactory generalization out of the source data distribution (OOD). In theory, the challenge remains unsolved even under the canonical setting of covariate shift for the linear model. This paper studies the foundational (high-dimensional) linear regression... | {
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2502.09050 | Leveraging Member-Group Relations via Multi-View Graph Filtering for
Effective Group Recommendation | [
"cs.IR",
"cs.AI",
"cs.IT",
"cs.LG",
"cs.SI",
"math.IT"
] | Group recommendation aims at providing optimized recommendations tailored to diverse groups, enabling groups to enjoy appropriate items. On the other hand, most existing group recommendation methods are built upon deep neural network (DNN) architectures designed to capture the intricate relationships between member-lev... | {
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2502.09051 | AIDE: Agentically Improve Visual Language Model with Domain Experts | [
"cs.CV",
"cs.AI",
"cs.MA"
] | The enhancement of Visual Language Models (VLMs) has traditionally relied on knowledge distillation from larger, more capable models. This dependence creates a fundamental bottleneck for improving state-of-the-art systems, particularly when no superior models exist. We introduce AIDE (Agentic Improvement through Domain... | {
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2502.09053 | Game Theory Meets Large Language Models: A Systematic Survey | [
"cs.AI",
"cs.GT",
"cs.LG"
] | Game theory establishes a fundamental framework for analyzing strategic interactions among rational decision-makers. The rapid advancement of large language models (LLMs) has sparked extensive research exploring the intersection of these two fields. Specifically, game-theoretic methods are being applied to evaluate and... | {
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2502.09054 | Cost-Saving LLM Cascades with Early Abstention | [
"cs.AI"
] | LLM cascades are based on the idea that processing all queries with the largest and most expensive LLMs is inefficient. Instead, cascades deploy small LLMs to answer the majority of queries, limiting the use of large and expensive LLMs to only the most difficult queries. This approach can significantly reduce costs wit... | {
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2502.09055 | Exploring the Needs of Practising Musicians in Co-Creative AI Through
Co-Design | [
"cs.HC",
"cs.AI"
] | Recent advances in generative AI music have resulted in new technologies that are being framed as co-creative tools for musicians with early work demonstrating their potential to add to music practice. While the field has seen many valuable contributions, work that involves practising musicians in the design and develo... | {
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2502.09056 | Adapting Language-Specific LLMs to a Reasoning Model in One Day via
Model Merging -- An Open Recipe | [
"cs.CL",
"cs.AI"
] | This paper investigates data selection and model merging methodologies aimed at incorporating advanced reasoning capabilities such as those of DeepSeek R1 into language-specific large language models (LLMs), with a particular focus on the Thai LLM. Our goal is to enhance the reasoning capabilities of language-specific ... | {
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2502.09057 | Vision-Language In-Context Learning Driven Few-Shot Visual Inspection
Model | [
"cs.CV"
] | We propose general visual inspection model using Vision-Language Model~(VLM) with few-shot images of non-defective or defective products, along with explanatory texts that serve as inspection criteria. Although existing VLM exhibit high performance across various tasks, they are not trained on specific tasks such as vi... | {
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2502.09058 | Unleashing the Power of Large Language Model for Denoising
Recommendation | [
"cs.IR"
] | Recommender systems are crucial for personalizing user experiences but often depend on implicit feedback data, which can be noisy and misleading. Existing denoising studies involve incorporating auxiliary information or learning strategies from interaction data. However, they struggle with the inherent limitations of e... | {
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2502.09060 | Anchor Sponsor Firms in Open Source Software Ecosystems | [
"cs.SE",
"cs.CY",
"cs.SI"
] | Firms are intensifying their involvement with open source software (OSS), going beyond contributing to individual projects and releasing their own core technologies as OSS. These technologies, from web frameworks to programming languages, are the foundations of large and growing ecosystems. Yet we know little about how... | {
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2502.09061 | CRANE: Reasoning with constrained LLM generation | [
"cs.PL",
"cs.LG"
] | Code generation, symbolic math reasoning, and other tasks require LLMs to produce outputs that are both syntactically and semantically correct. Constrained LLM generation is a promising direction to enforce adherence to formal grammar, but prior works have empirically observed that strict enforcement of formal constrai... | {
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2502.09064 | StyleBlend: Enhancing Style-Specific Content Creation in Text-to-Image
Diffusion Models | [
"cs.CV"
] | Synthesizing visually impressive images that seamlessly align both text prompts and specific artistic styles remains a significant challenge in Text-to-Image (T2I) diffusion models. This paper introduces StyleBlend, a method designed to learn and apply style representations from a limited set of reference images, enabl... | {
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2502.09065 | Lowering the Error Floor of Error Correction Code Transformer | [
"cs.IT",
"math.IT"
] | With the success of transformer architectures across diverse applications, the error correction code transformer (ECCT) has gained significant attention for its superior decoding performance. In spite of its advantages, the error floor phenomenon in ECCT decoding remains unexplored. We present the first investigation o... | {
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2502.09067 | FlowAR: une plateforme uniformis\'ee pour la reconnaissance des
activit\'es humaines \`a partir de capteurs binaires | [
"cs.LG"
] | This demo showcases a platform for developing human activity recognition (AR) systems, focusing on daily activities using sensor data, like binary sensors. With a data-driven approach, this platform, named FlowAR, features a three-step pipeline (flow): data cleaning, segmentation, and personalized classification. Its m... | {
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2502.09073 | Enhancing RAG with Active Learning on Conversation Records: Reject
Incapables and Answer Capables | [
"cs.CL"
] | Retrieval-augmented generation (RAG) is a key technique for leveraging external knowledge and reducing hallucinations in large language models (LLMs). However, RAG still struggles to fully prevent hallucinated responses. To address this, it is essential to identify samples prone to hallucination or guide LLMs toward co... | {
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} |
2502.09075 | PTZ-Calib: Robust Pan-Tilt-Zoom Camera Calibration | [
"cs.CV"
] | In this paper, we present PTZ-Calib, a robust two-stage PTZ camera calibration method, that efficiently and accurately estimates camera parameters for arbitrary viewpoints. Our method includes an offline and an online stage. In the offline stage, we first uniformly select a set of reference images that sufficiently ove... | {
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} |
2502.09079 | Quantifying Cryptocurrency Unpredictability: A Comprehensive Study of
Complexity and Forecasting | [
"q-fin.ST",
"cs.LG",
"q-fin.CP"
] | This paper offers a thorough examination of the univariate predictability in cryptocurrency time-series. By exploiting a combination of complexity measure and model predictions we explore the cryptocurrencies time-series forecasting task focusing on the exchange rate in USD of Litecoin, Binance Coin, Bitcoin, Ethereum,... | {
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2502.09080 | BevSplat: Resolving Height Ambiguity via Feature-Based Gaussian
Primitives for Weakly-Supervised Cross-View Localization | [
"cs.CV"
] | This paper addresses the problem of weakly supervised cross-view localization, where the goal is to estimate the pose of a ground camera relative to a satellite image with noisy ground truth annotations. A common approach to bridge the cross-view domain gap for pose estimation is Bird's-Eye View (BEV) synthesis. Howeve... | {
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} |
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