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.

SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models

Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders

Rethinking Metrics and Diffusion Architecture for 3D Point Cloud Generation

DeepEyesV2: Toward Agentic Multimodal Model

MERaLiON-SER: Robust Speech Emotion Recognition Model for English and SEA Languages

Minimal and Mechanistic Conditions for Behavioral Self-Awareness in LLMs

multiMentalRoBERTa: A Fine-tuned Multiclass Classifier for Mental Health Disorder

Addressing divergent representations from causal interventions on neural networks

Alternative Fairness and Accuracy Optimization in Criminal Justice

Leveraging LLM-based agents for social science research: insights from citation network simulations

Conversational Collective Intelligence (CCI) using Hyperchat AI in a Real-world Forecasting Task

Explaining Human Choice Probabilities with Simple Vector Representations

Development of the Bioinspired Tendon-Driven DexHand 021 with Proprioceptive Compliance Control

GraphCliff: Short-Long Range Gating for Subtle Differences but Critical Changes

AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models

MM-UNet: Morph Mamba U-shaped Convolutional Networks for Retinal Vessel Segmentation

RobustFSM: Submodular Maximization in Federated Setting with Malicious Clients

Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch

Reg-DPO: SFT-Regularized Direct Preference Optimization with GT-Pair for Improving Video Generation

Reasoning Planning for Language Models

Aligning Brain Signals with Multimodal Speech and Vision Embeddings

Calibrating and Rotating: A Unified Framework for Weight Conditioning in PEFT

FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models

Beyond Data Scarcity Optimizing R3GAN for Medical Image Generation from Small Datasets

Inside CORE-KG: Evaluating Structured Prompting and Coreference Resolution for Knowledge Graphs

Learning from N-Tuple Data with M Positive Instances: Unbiased Risk Estimation and Theoretical Guarantees

Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning

DynaSpec: Context-aware Dynamic Speculative Sampling for Large-Vocabulary Language Models

Meronymic Ontology Extraction via Large Language Models

Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach

DPCformer: An Interpretable Deep Learning Model for Genomic Prediction in Crops

The Markovian Thinker

scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis

Data-Centric Elastic Pipeline Parallelism for Efficient Long-Context LLM Training

Security-aware Semantic-driven ISAC via Paired Adversarial Residual Networks

TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding

Pure Vision Language Action (VLA) Models: A Comprehensive Survey

UniPixel: Unified Object Referring and Segmentation for Pixel-Level Visual Reasoning

Assisting the Grading of a Handwritten General Chemistry Exam with Artificial Intelligence

Privacy-Preserving Personalization in Education: A Federated Recommender System for Student Performance Prediction

UNO: Unifying One-stage Video Scene Graph Generation via Object-Centric Visual Representation Learning

Normality and the Turing Test

GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs

Cross-Platform E-Commerce Product Categorization and Recategorization: A Multimodal Hierarchical Classification Approach

ANO : Faster is Better in Noisy Landscape

Generative Medical Event Models Improve with Scale

ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning

LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data

Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models

Rethinking Tokenization for Rich Morphology: The Dominance of Unigram over BPE and Morphological Alignment

Discovering Spatial Correlations of Earth Observations for weather forecasting by using Graph Structure Learning

Forecasting When to Forecast: Accelerating Diffusion Models with Confidence-Gated Taylor

Universal Neurons in GPT-2: Emergence, Persistence, and Functional Impact

Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning

RaGS: Unleashing 3D Gaussian Splatting from 4D Radar and Monocular Cues for 3D Object Detection

Controllable Hybrid Captioner for Improved Long-form Video Understanding

Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning

When Person Re-Identification Meets Event Camera: A Benchmark Dataset and An Attribute-guided Re-Identification Framework

Evaluating LLM-based Workflows for Switched-Mode Power Supply Design

Monitoring Risks in Test-Time Adaptation

White-Basilisk: A Hybrid Model for Code Vulnerability Detection

Bridging the Plausibility-Validity Gap by Fine-Tuning a Reasoning-Enhanced LLM for Chemical Synthesis and Discovery

CoRe: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks

DP-Fusion: Token-Level Differentially Private Inference for Large Language Models

Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks

GPT, But Backwards: Exactly Inverting Language Model Outputs

Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs

ReCode: Updating Code API Knowledge with Reinforcement Learning

Time-Prompt: Integrated Heterogeneous Prompts for Unlocking LLMs in Time Series Forecasting

Sekai: A Video Dataset towards World Exploration

Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion

Towards Competent AI for Fundamental Analysis in Finance: A Benchmark Dataset and Evaluation

A Fairness-Aware Strategy for B5G Physical-layer Security Leveraging Reconfigurable Intelligent Surfaces

Bridging Weakly-Supervised Learning and VLM Distillation: Noisy Partial Label Learning for Efficient Downstream Adaptation

Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologies

GRAM: Spatial general-purpose audio representation models for real-world applications

Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications

VideoCAD: A Dataset and Model for Learning Long-Horizon 3D CAD UI Interactions from Video

When Language Shapes Thought: Cross-Lingual Transfer of Factual Knowledge in Question Answering

Rethinking Text-based Protein Understanding: Retrieval or LLM?

Language Model Distillation: A Temporal Difference Imitation Learning Perspective

TextDiffuser-RL: Efficient and Robust Text Layout Optimization for High-Fidelity Text-to-Image Synthesis

Guided Diffusion Sampling on Function Spaces with Applications to PDEs

The Energy Cost of Reasoning: Analyzing Energy Usage in LLMs with Test-time Compute

DNOI-4DRO: Deep 4D Radar Odometry with Differentiable Neural-Optimization Iterations

When Bias Helps Learning: Bridging Initial Prejudice and Trainability

HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation

X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real

Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering

Video CLIP Model for Multi-View Echocardiography Interpretation

Quantum Doubly Stochastic Transformers

ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness

Parameter-Free Fine-tuning via Redundancy Elimination for Vision Foundation Models

Accelerating LLM Inference Throughput via Asynchronous KV Cache Prefetching

MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation

How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence

On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation

Quantitative Evaluation of Quantum/Classical Neural Network Using a Game Solver Metric

Simulator Ensembles for Trustworthy Autonomous Driving Testing

Continual Pre-training of MoEs: How robust is your router?

DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation

Created by

Haebom

저자

Chengkun Wei, Weixian Li, Gong Chen, Wenzhi Chen

개요

본 논문은 차등적 개인정보 보호 심층 학습을 위한 널리 사용되는 기법인 차등적 개인정보 보호 확률적 경사 하강법(DP-SGD)에서 최적 클리핑 임계값 C 선택의 어려움을 해결하기 위해 동적 클리핑 DP-SGD(DC-SGD) 프레임워크를 제안합니다. DC-SGD는 차등적 개인정보 보호 히스토그램을 활용하여 기울기 놈 분포를 추정하고 클리핑 임계값 C를 동적으로 조정하는 두 가지 새로운 메커니즘, DC-SGD-P와 DC-SGD-E를 포함합니다. DC-SGD-P는 기울기 놈의 백분위수를 기반으로 클리핑 임계값을 조정하고, DC-SGD-E는 기울기의 예상 제곱 오차를 최소화하여 C를 최적화합니다. 이러한 동적 조정을 통해 하이퍼파라미터 튜닝 부담을 크게 줄이며, 이미지 분류 및 자연어 처리 등 다양한 심층 학습 작업에 대한 광범위한 실험을 통해 하이퍼파라미터 튜닝 속도가 최대 9배까지 향상됨을 보여줍니다. 특히 DC-SGD-E는 동일한 개인정보 보호 예산 하에서 CIFAR10에서 DP-SGD보다 10.62% 향상된 정확도를 달성합니다. 또한, Adam 최적화 프로그램과의 원활한 통합을 보여주는 엄격한 이론적 개인정보 보호 및 수렴 분석을 수행합니다.

시사점, 한계점

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시사점:

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DP-SGD의 하이퍼파라미터 튜닝 부담을 크게 줄이는 효율적인 동적 클리핑 메커니즘(DC-SGD)을 제시합니다.

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DC-SGD-E는 동일한 개인정보 보호 예산 하에서 기존 DP-SGD보다 향상된 정확도를 달성합니다.

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하이퍼파라미터 튜닝 속도를 최대 9배까지 향상시킵니다.

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Adam 최적화 프로그램과의 원활한 통합을 보장하는 이론적 분석을 제공합니다.

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차등적 개인정보 보호 심층 학습의 실용적인 해결책을 제시합니다.

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한계점:

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본 논문에서 제시된 DC-SGD의 일반적인 성능과 효율성은 다양한 데이터셋과 모델 아키텍처에 대한 추가적인 실험을 통해 더욱 검증될 필요가 있습니다.

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DC-SGD의 동적 클리핑 메커니즘이 모든 종류의 심층 학습 작업에 효과적인지에 대한 추가적인 연구가 필요합니다.

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개인정보 보호와 정확도 사이의 최적의 균형점을 찾는 방법에 대한 추가적인 연구가 필요합니다.

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