Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

Private Frequency Estimation Via Residue Number Systems

A Unified Convergence Analysis for Semi-Decentralized Learning: Sampled-to-Sampled vs. Sampled-to-All Communication

Privacy Challenges and Solutions in Retrieval-Augmented Generation-Enhanced LLMs for Healthcare Chatbots: A Review of Applications, Risks, and Future Directions

Virtual Width Networks

Efficient Reinforcement Learning for Zero-Shot Coordination in Evolving Games

Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types

DialogGraph-LLM: Graph-Informed LLMs for End-to-End Audio Dialogue Intent Recognition

A Style is Worth One Code: Unlocking Code-to-Style Image Generation with Discrete Style Space

MonkeyOCR v1.5 Technical Report: Unlocking Robust Document Parsing for Complex Patterns

DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile

BhashaKritika: Building Synthetic Pretraining Data at Scale for Indic Languages

T2IBias: Uncovering Societal Bias Encoded in the Latent Space of Text-to-Image Generative Models

History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting

C$^3$TG: Conflict-aware, Composite, and Collaborative Controlled Text Generation

Good-for-MDP State Reduction for Stochastic LTL Planning

PAN: A World Model for General, Interactable, and Long-Horizon World Simulation

HQ-SVC: Towards High-Quality Zero-Shot Singing Voice Conversion in Low-Resource Scenarios

MARC: Multimodal and Multi-Task Agentic Retrieval-Augmented Generation for Cold-Start Recommender System

Constrained and Robust Policy Synthesis with Satisfiability-Modulo-Probabilistic-Model-Checking

Self-Correction Distillation for Structured Data Question Answering

Test-driven Reinforcement Learning

Learning Quantized Continuous Controllers for Integer Hardware

Differentiated Directional Intervention A Framework for Evading LLM Safety Alignment

Breaking the Dyadic Barrier: Rethinking Fairness in Link Prediction Beyond Demographic Parity

Ghost in the Transformer: Tracing LLM Lineage with SVD-Fingerprint

Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets

Measuring Model Performance in the Presence of an Intervention

Ada-FCN: Adaptive Frequency-Coupled Network for fMRI-Based Brain Disorder Classification

MusRec: Zero-Shot Text-to-Music Editing via Rectified Flow and Diffusion Transformers

Proto-LeakNet: Towards Signal-Leak Aware Attribution in Synthetic Human Face Imagery

DMSORT: An efficient parallel maritime multi-object tracking architecture for unmanned vessel platforms

Multi-Personality Generation of LLMs at Decoding-time

LeMiCa: Lexicographic Minimax Path Caching for Efficient Diffusion-Based Video Generation

Comparative Study of UNet-based Architectures for Liver Tumor Segmentation in Multi-Phase Contrast-Enhanced Computed Tomography

Reflections on the Reproducibility of Commercial LLM Performance in Empirical Software Engineering Studies

MMEdge: Accelerating On-device Multimodal Inference via Pipelined Sensing and Encoding

VeriStruct: AI-assisted Automated Verification of Data-Structure Modules in Verus

Mutual Wanting in Human--AI Interaction: Empirical Evidence from Large-Scale Analysis of GPT Model Transitions

Geometric Algorithms for Neural Combinatorial Optimization with Constraints

SelecTKD: Selective Token-Weighted Knowledge Distillation for LLMs

Integrating Genomics into Multimodal EHR Foundation Models

CompressionAttack: Exploiting Prompt Compression as a New Attack Surface in LLM-Powered Agents

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata

Online Mixture of Experts: No-Regret Learning for Optimal Collective Decision-Making

When Facts Change: Probing LLMs on Evolving Knowledge with evolveQA

Local Guidance for Configuration-Based Multi-Agent Pathfinding

3D Optimization for AI Inference Scaling: Balancing Accuracy, Cost, and Latency

SparseWorld: A Flexible, Adaptive, and Efficient 4D Occupancy World Model Powered by Sparse and Dynamic Queries

InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training

DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios

DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models

Dedelayed: Deleting remote inference delay via on-device correction

From Delegates to Trustees: How Optimizing for Long-Term Interests Shapes Bias and Alignment in LLM

On the Fairness of Privacy Protection: Measuring and Mitigating the Disparity of Group Privacy Risks for Differentially Private Machine Learning

A Human Behavioral Baseline for Collective Governance in Software Projects

Implicit-Knowledge Visual Question Answering with Structured Reasoning Traces

Reasoning under Vision: Understanding Visual-Spatial Cognition in Vision-Language Models for CAPTCHA

Global Convergence of Policy Gradient for Entropy Regularized Linear-Quadratic Control with Multiplicative Noise

Hierarchical Generalized Category Discovery for Brain Tumor Classification in Digital Pathology

Does Bigger Mean Better? Comparitive Analysis of CNNs and Biomedical Vision Language Modles in Medical Diagnosis

A Measurement Study of Model Context Protocol Ecosystem

Energy Guided Geometric Flow Matching

HyperCore: Coreset Selection under Noise via Hypersphere Models

VIR-Bench: Evaluating Geospatial and Temporal Understanding of MLLMs via Travel Video Itinerary Reconstruction

Understanding Post-Training Structural Changes in Large Language Models

Highly Imbalanced Regression with Tabular Data in SEP and Other Applications

MedFact: Benchmarking the Fact-Checking Capabilities of Large Language Models on Chinese Medical Texts

Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing

A GPU-Accelerated RAG-Based Telegram Assistant for Supporting Parallel Processing Students

Probabilistic Robustness Analysis in High Dimensional Space: Application to Semantic Segmentation Network

A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data

Realism Control One-step Diffusion for Real-World Image Super-Resolution

An Ontology-Based Approach to Optimizing Geometry Problem Sets for Skill Development

AHAMask: Reliable Task Specification for Large Audio Language Models without Instructions

NoLBERT: A No Lookahead(back) Foundational Language Model

SoK: Large Language Model Copyright Auditing via Fingerprinting

PAX-TS: Model-agnostic multi-granular explanations for time series forecasting via localized perturbations

Multi-Metric Preference Alignment for Generative Speech Restoration

Error analysis for the deep Kolmogorov method

Interpreting the Effects of Quantization on LLMs

Few-shot Class-incremental Fault Diagnosis by Preserving Class-Agnostic Knowledge with Dual-Granularity Representations

STA-GANN: A Valid and Generalizable Spatio-Temporal Kriging Approach

Unintended Misalignment from Agentic Fine-Tuning: Risks and Mitigation

edgeVLM: Cloud-edge Collaborative Real-time VLM based on Context Transfer

What One Cannot, Two Can: Two-Layer Transformers Provably Represent Induction Heads on Any-Order Markov Chains

Efficient Reasoning for Large Reasoning Language Models via Certainty-Guided Reflection Suppression

HierarchicalPrune: Position-Aware Compression for Large-Scale Diffusion Models

NLP Methods May Actually Be Better Than Professors at Estimating Question Difficulty

Landsat30-AU: A Vision-Language Dataset for Australian Landsat Imagery

Intention-Guided Cognitive Reasoning for Egocentric Long-Term Action Anticipation

NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System

MoCHA: Advanced Vision-Language Reasoning with MoE Connector and Hierarchical Group Attention

Exploiting Synergistic Cognitive Biases to Bypass Safety in LLMs

TransPrune: Token Transition Pruning for Efficient Large Vision-Language Model

Machine Unlearning of Traffic State Estimation and Prediction

Benchmarking LLM Privacy Recognition for Social Robot Decision Making

Dream, Lift, Animate: From Single Images to Animatable Gaussian Avatars

AI-Native Open RAN for Non-Terrestrial Networks: An Overview

Sequential Attention-based Sampling for Histopathological Analysis

RAG-R1: Incentivizing the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism

Architecture independent generalization bounds for overparametrized deep ReLU networks

Created by

Haebom

저자

Thomas Chen, Chun-Kai Kevin Chien, Patricia Munoz Ewald, Andrew G. Moore

개요

과매개화된 신경망이 과매개화 수준과 VC 차원과 무관하게 독립적인 테스트 오류로 일반화될 수 있음을 증명한 논문입니다. 테스트 및 훈련 세트의 메트릭 기하학, 활성화 함수의 규칙성, 가중치의 연산자 노름 및 바이어스의 노름에만 의존하는 명시적인 경계를 증명합니다. 입력 공간 차원에 의해 제한된 훈련 샘플 크기를 갖는 과매개화된 심층 ReLU 네트워크의 경우, 경사 하강법을 사용하지 않고 제로 손실 최소화기를 명시적으로 구성하고, 일반화 오류가 네트워크 아키텍처와 무관함을 증명합니다.

시사점, 한계점

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시사점: 과매개화된 신경망의 일반화 성능에 대한 새로운 이해를 제공합니다. 과매개화 수준과 VC 차원이 일반화 성능에 영향을 미치지 않을 수 있음을 보여줍니다. 메트릭 기하학과 활성화 함수의 규칙성이 일반화 성능에 중요한 역할을 한다는 것을 시사합니다. 경사 하강법 없이도 제로 손실 최소화기를 구성할 수 있음을 보여줍니다.

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한계점: 증명이 특정 활성화 함수(ReLU)와 제한된 훈련 샘플 크기에 의존합니다. 실제 데이터셋에 대한 일반화 가능성은 추가 연구가 필요합니다. 제로 손실 최소화기 구성 방법이 실제 적용에 얼마나 효율적인지는 불명확합니다.

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