Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
본 페이지는 Google Gemini를 활용해 요약 정리하며, 비영리로 운영 됩니다.
논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

Aggregating Concepts of Accuracy and Fairness in Prediction Algorithms

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning

Graph neural networks and MSO

Generative Pre-trained Autoregressive Diffusion Transformer

SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models

Towards user-centered interactive medical image segmentation in VR with an assistive AI agent

Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression

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

UniVLA: Learning to Act Anywhere with Task-centric Latent Actions

Assessing Tenstorrent's RISC-V MatMul Acceleration Capabilities

Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection

Understanding In-context Learning of Addition via Activation Subspaces

Risk-sensitive Reinforcement Learning Based on Convex Scoring Functions

Intelligently Augmented Contrastive Tensor Factorization: Empowering Multi-dimensional Time Series Classification in Low-Data Environments

Modeling Human Behavior in a Strategic Network Game with Complex Group Dynamics

GPU Performance Portability needs Autotuning

RM-R1: Reward Modeling as Reasoning

Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments

Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction

WildFireCan-MMD: A Multimodal Dataset for Classification of User-Generated Content During Wildfires in Canada

CLASH: Evaluating Language Models on Judging High-Stakes Dilemmas from Multiple Perspectives

Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges

Flexible Graph Similarity Computation With A Proactive Optimization Strategy

CryoSAMU: Enhancing 3D Cryo-EM Density Maps of Protein Structures at Intermediate Resolution with Structure-Aware Multimodal U-Nets

PromptMobile: Efficient Promptus for Low Bandwidth Mobile Video Streaming

Implicit Bias-Like Patterns in Reasoning Models

EMMOE: A Comprehensive Benchmark for Embodied Mobile Manipulation in Open Environments

Temporal Triplane Transformers as Occupancy World Models

The Lazy Student's Dream: ChatGPT Passing an Engineering Course on Its Own

Atlas: A Framework for ML Lifecycle Provenance & Transparency

Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control

Illegal Waste Detection in Remote Sensing Images: A Case Study

ARR: Question Answering with Large Language Models via Analyzing, Retrieving, and Reasoning

ImprovNet -- Generating Controllable Musical Improvisations with Iterative Corruption Refinement

Lightspeed Geometric Dataset Distance via Sliced Optimal Transport

Benchmarking Generative AI for Scoring Medical Student Interviews in Objective Structured Clinical Examinations (OSCEs)

Representation Convergence: Mutual Distillation is Secretly a Form of Regularization

Goal-Conditioned Supervised Learning for Multi-Objective Recommendation

Not All Adapters Matter: Selective Adapter Freezing for Memory-Efficient Fine-Tuning of Language Models

Simple and Provable Scaling Laws for the Test-Time Compute of Large Language Models

KBAlign: Efficient Self Adaptation on Specific Knowledge Bases

Natural Language Reinforcement Learning

Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study AI for ECG to CMR Translation Study

TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection

Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs

Temporal-Difference Variational Continual Learning

FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering

Time Awareness in Large Language Models: Benchmarking Fact Recall Across Time

Self-supervised Learning for Acoustic Few-Shot Classification

Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learning

SAKR: Enhancing Retrieval-Augmented Generation via Streaming Algorithm and K-Means Clustering

Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners

Beyond Next Token Prediction: Patch-Level Training for Large Language Models

PersLLM: A Personified Training Approach for Large Language Models

PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling

Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models

RoBERTa-BiLSTM: A Context-Aware Hybrid Model for Sentiment Analysis

Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

Aligning Transformers with Continuous Feedback via Energy Rank Alignment

Large Language Models for Cyber Security: A Systematic Literature Review

Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model

CreativeSynth: Cross-Art-Attention for Artistic Image Synthesis with Multimodal Diffusion

LLM A*: Human in the Loop Large Language Models Enabled A* Search for Robotics

Neurodivergent Influenceability as a Contingent Solution to the AI Alignment Problem

AssertionForge: Enhancing Formal Verification Assertion Generation with Structured Representation of Specifications and RTL

Demonstrating specification gaming in reasoning models

SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions

MapExplorer: New Content Generation from Low-Dimensional Visualizations

Addressing and Visualizing Misalignments in Human Task-Solving Trajectories

AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents

Learning Progress Driven Multi-Agent Curriculum

Neural Thermodynamic Laws for Large Language Model Training

MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning

Does Feasibility Matter? Understanding the Impact of Feasibility on Synthetic Training Data

Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning

LibIQ: Toward Real-Time Spectrum Classification in O-RAN dApps

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation

Multi-Token Prediction Needs Registers

PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and Models

UniEval: Unified Holistic Evaluation for Unified Multimodal Understanding and Generation

Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps

Superposition Yields Robust Neural Scaling

SEAL: Searching Expandable Architectures for Incremental Learning

Vision language models have difficulty recognizing virtual objects

Are Large Language Models Robust in Understanding Code Against Semantics-Preserving Mutations?

IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-Tuning

PIF: Anomaly detection via preference embedding

Learned Lightweight Smartphone ISP with Unpaired Data

Visual Fidelity Index for Generative Semantic Communications with Critical Information Embedding

Rethinking Repetition Problems of LLMs in Code Generation

Inconsistency Handling in DatalogMTL

Uncovering Magnetic Phases with Synthetic Data and Physics-Informed Training

Schreier-Coset Graph Propagation

Multi-Agent Path Finding For Large Agents Is Intractable

Are Sparse Autoencoders Useful for Java Function Bug Detection?

ILIF: Temporal Inhibitory Leaky Integrate-and-Fire Neuron for Overactivation in Spiking Neural Networks

FactsR: A Safer Method for Producing High Quality Healthcare Documentation

SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and $\mathcal{O}(T)$ Complexity

Uniform Loss vs. Specialized Optimization: A Comparative Analysis in Multi-Task Learning

Emergence of Structure in Ensembles of Random Neural Networks

ILIF: Temporal Inhibitory Leaky Integrate-and-Fire Neuron for Overactivation in Spiking Neural Networks

Created by

Haebom

저자

Kai Sun, Peibo Duan, Levin Kuhlmann, Beilun Wang, Bin Zhang

개요

본 논문은 에너지 효율적인 이벤트 기반 처리 및 생물학적 타당성으로 인해 주목받고 있는 스파이킹 신경망(SNN)의 훈련에 초점을 맞추고 있습니다. SNN을 역전파를 통해 훈련하기 위해서는 미분 불가능한 스파이크 함수를 근사하는 대리 기울기(surrogate gradient)를 사용하는데, 이는 활성화 역치 근처의 좁은 범위(감마, γ) 내에서만 0이 아닌 도함수를 유지합니다. 논문은 감마의 크기에 따른 과활성화(에너지 소모 증가) 및 기울기 소멸(시간적 의존성 약화) 문제를 '감마의 딜레마'로 정의하고, 이를 해결하기 위해 생물학적 억제 메커니즘에서 영감을 받은 시간적 억제 누출-적분-발화(ILIF) 뉴런 모델을 제안합니다. ILIF 모델은 막 전위와 전류에 대한 상호 연결된 억제 유닛을 통합하여 과활성화를 완화하면서 기울기 전파를 유지합니다. 이론적 분석과 다양한 데이터셋에 대한 실험을 통해 ILIF가 감마의 딜레마를 극복하고 발화율 감소를 통한 에너지 효율 향상, 훈련 안정화 및 정확도 향상에 효과적임을 보여줍니다. 소스 코드는 github.com/kaisun1/ILIF에서 이용 가능합니다.

시사점, 한계점

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시사점:

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SNN 훈련에서의 '감마의 딜레마' 문제를 명확히 제시하고, 이를 해결하기 위한 효과적인 방법을 제안.

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ILIF 모델을 통해 SNN의 에너지 효율을 향상시키고 훈련 안정성과 정확도를 높일 수 있음을 실험적으로 증명.

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생물학적 메커니즘을 모방한 새로운 뉴런 모델을 제시하여 SNN 연구에 새로운 가능성 제시.

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한계점:

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ILIF 모델의 효과가 다양한 SNN 아키텍처 및 데이터셋에 대해 얼마나 일반화될 수 있는지 추가적인 연구 필요.

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생물학적 타당성을 높이기 위한 추가적인 개선 여지 존재.

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ILIF 모델의 계산 비용 및 복잡도에 대한 분석 필요.

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