Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Horus: A Protocol for Trustless Delegation Under Uncertainty

Reducing Variability of Multiple Instance Learning Methods for Digital Pathology

Positioning AI Tools to Support Online Harm Reduction Practice: Applications and Design Directions

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues

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

BioPars: A Pretrained Biomedical Large Language Model for Persian Biomedical Text Mining

TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design

Towards Safety Evaluations of Theory of Mind in Large Language Models

Fair Algorithms with Probing for Multi-Agent Multi-Armed Bandits

GraphGSOcc: Semantic-Geometric Graph Transformer with Dynamic-Static Decoupling for 3D Gaussian Splatting-based Occupancy Prediction

Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments

15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained Networks

Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers

BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning

On the Fundamental Impossibility of Hallucination Control in Large Language Models

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems

Real-Time Blind Defocus Deblurring for Earth Observation: The IMAGIN-e Mission Approach

Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modeling

Self-reflective Uncertainties: Do LLMs Know Their Internal Answer Distribution?

FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization

Pre-training Large Memory Language Models with Internal and External Knowledge

Towards Universal Semantics With Large Language Models

Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations

Enhancing Robustness to Missing Modalities through Clustered Federated Learning

Perceiving Beyond Language Priors: Enhancing Visual Comprehension and Attention in Multimodal Models

LZ Penalty: An information-theoretic repetition penalty for autoregressive language models

Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond

TARO: Timestep-Adaptive Representation Alignment with Onset-Aware Conditioning for Synchronized Video-to-Audio Synthesis

Recursive Training Loops in LLMs: How training data properties modulate distribution shift in generated data?

Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin

Concat-ID: Towards Universal Identity-Preserving Video Synthesis

How Metacognitive Architectures Remember Their Own Thoughts: A Systematic Review

SFO: Piloting VLM Feedback for Offline RL

Towards Efficient Educational Chatbots: Benchmarking RAG Frameworks

KatFishNet: Detecting LLM-Generated Korean Text through Linguistic Feature Analysis

Distribution Matching for Self-Supervised Transfer Learning

A Baseline Method for Removing Invisible Image Watermarks using Deep Image Prior

SKIL: Semantic Keypoint Imitation Learning for Generalizable Data-efficient Manipulation

AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks

A Framework for Mining Collectively-Behaving Bots in MMORPGs

Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions

A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs

There and Back Again: On the relation between Noise and Image Inversions in Diffusion Models

Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Models

Contrastive Learning and Adversarial Disentanglement for Privacy-Aware Task-Oriented Semantic Communication

Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization

NegMerge: Sign-Consensual Weight Merging for Machine Unlearning

Drug Discovery SMILES-to-Pharmacokinetics Diffusion Models with Deep Molecular Understanding

Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems

Backdooring Bias (B^2) into Stable Diffusion Models

Embodied Instruction Following in Unknown Environments

Improving Consistency Models with Generator-Augmented Flows

OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors

Divergent Creativity in Humans and Large Language Models

SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Embedded AI Systems

Squat: Quant Small Language Models on the Edge

Dataset Distillation via the Wasserstein Metric

The Boolean Solution Problem from the Perspective of Predicate Logic -- Extended Version

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess

World-aware Planning Narratives Enhance Large Vision-Language Model Planner

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?

MMLU-Reason: Benchmarking Multi-Task Multi-modal Language Understanding and Reasoning

Adapting Probabilistic Risk Assessment for AI

Beating Transformers using Synthetic Cognition

MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow

Using Large Language Models to Categorize Strategic Situations and Decipher Motivations Behind Human Behaviors

ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation

MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification

DREAMS: A python framework for Training Deep Learning Models on EEG Data with Model Card Reporting for Medical Applications

Human Mobility Modeling with Household Coordination Activities under Limited Information via Retrieval-Augmented LLMs

Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment

AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation

Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation

How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks

SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars

Adaptability of ASR Models on Low-Resource Language: A Comparative Study of Whisper and Wav2Vec-BERT on Bangla

Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection

End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning

Gradient-Adaptive Policy Optimization: Towards Multi-Objective Alignment of Large Language Models

AI4Research: A Survey of Artificial Intelligence for Scientific Research

Towards Foundation Auto-Encoders for Time-Series Anomaly Detection

Bridging UI Design and chatbot Interactions: Applying Form-Based Principles to Conversational Agents

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling

MILP-SAT-GNN: Yet Another Neural SAT Solver

Empowering Manufacturers with Privacy-Preserving AI Tools: A Case Study in Privacy-Preserving Machine Learning to Solve Real-World Problems

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs

How Do Vision-Language Models Process Conflicting Information Across Modalities?

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging

Probing Evaluation Awareness of Language Models

MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining

BranchNet: A Neuro-Symbolic Learning Framework for Structured Multi-Class Classification

GPU-based complete search for nonlinear minimization subject to bounds

Enhanced Generative Model Evaluation with Clipped Density and Coverage

Tuning without Peeking: Provable Privacy and Generalization Bounds for LLM Post-Training

ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving

Towards culturally-appropriate conversational AI for health in the majority world: An exploratory study with citizens and professionals in Latin America

Studying and Improving Graph Neural Network-based Motif Estimation

Created by

Haebom

저자

Pedro C. Vieira, Miguel E. P. Silva, Pedro Manuel Pinto Ribeiro

개요

본 논문은 그래프 신경망(GNN)을 이용한 네트워크 모티프 유의성 프로파일(SP) 예측에 대한 연구를 제시합니다. 기존의 서브그래프 빈도 추정에 기반한 방식과 달리, 본 연구는 서브그래프 빈도 추정과 독립적인 SP 추정을 다중 타겟 회귀 문제로 재정의하여 해석력, 안정성, 확장성을 향상시켰습니다. 합성 데이터와 실제 그래프 데이터를 사용한 실험을 통해 1-WL 제한 모델이 정확한 SP 추정에는 어려움을 겪지만, 예측된 SP와 합성 생성기의 SP를 비교하여 그래프 생성 과정을 근사적으로 일반화할 수 있음을 보였습니다. 서브그래프 계수를 통한 모티프 추정의 이론적 한계를 극복하는 데 직접적인 SP 추정이 기여할 수 있음을 시사합니다.

시사점, 한계점

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시사점:

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GNN을 이용한 네트워크 모티프 SP 예측에 대한 새로운 접근 방식 제시

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서브그래프 빈도 추정과 독립적인 SP 추정 방식의 효용성 검증

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1-WL 제한 모델의 한계에도 불구하고, 그래프 생성 과정 근사에 대한 가능성 제시

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서브그래프 계수 기반 모티프 추정의 한계 극복 가능성 제시

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한계점:

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1-WL 제한 모델의 정확한 SP 추정 성능 저하

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대규모 그래프에 대한 확장성 검증은 추가 연구 필요할 수 있음

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제안된 방법의 일반화 성능에 대한 추가적인 분석 필요

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