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Daily Arxiv
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.
A Systematic Review of Human-AI Co-Creativity
DFVEdit: Conditional Delta Flow Vector for Zero-shot Video Editing
Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research
MUPA: Towards Multi-Path Agentic Reasoning for Grounded Video Question Answering
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization
How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
Vision Transformers Don't Need Trained Registers
Eye of Judgement: Dissecting the Evaluation of Russian-speaking LLMs with POLLUX
Maximizing Confidence Alone Improves Reasoning
EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models
Improving LLM Outputs Against Jailbreak Attacks with Expert Model Integration
Personalized Robotic Object Rearrangement from Scene Context
Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs
OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis
Explicit neural network classifiers for non-separable data
USM-VC: Mitigating Timbre Leakage with Universal Semantic Mapping Residual Block for Voice Conversion
Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation
LoopGen: Training-Free Loopable Music Generation
Automated detection of atomicity violations in large-scale systems
Grammar and Gameplay-aligned RL for Game Description Generation with LLMs
Generative AI for Software Architecture. Applications, Challenges, and Future Directions
English K_Quantization of LLMs Does Not Disproportionately Diminish Multilingual Performance
Heuristics for AI-driven Graphical Asset Generation Tools in Game Design and Development Pipelines: A User-Centred Approach
Collective Reasoning Among LLMs: A Framework for Answer Validation Without Ground Truth
Multi-Turn Code Generation Through Single-Step Rewards
Round Attention: A Novel Round-Level Attention Mechanism to Accelerate LLM Inference
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding
AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers
MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot
Generative Data Mining with Longtail-Guided Diffusion
Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation
No More Sliding Window: Efficient 3D Medical Image Segmentation with Differentiable Top-k Patch Sampling
Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency
End-to-End Long Document Summarization using Gradient Caching
Refining Salience-Aware Sparse Fine-Tuning Strategies for Language Models
KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment
EUR/USD Exchange Rate Forecasting incorporating Text Mining Based on Pre-trained Language Models and Deep Learning Methods
Large-Scale Multirobot Coverage Path Planning on Grids With Path Deconfliction
Dynamic Adaptive Rank Space Exploration for Efficient Sentiment Analysis with Large Language Models
Federated Data-Efficient Instruction Tuning for Large Language Models
QT-DoG: Quantization-aware Training for Domain Generalization
Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies
Stability of Primal-Dual Gradient Flow Dynamics for Multi-Block Convex Optimization Problems
LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagation
The Mamba in the Llama: Distilling and Accelerating Hybrid Models
EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods
Dynamic Adaptive Optimization for Effective Sentiment Analysis Fine-Tuning on Large Language Models
Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale
CAPM: Fast and Robust Verification on Maxpool-based CNN via Dual Network
MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance
From Data Quality for AI to AI for Data Quality: A Systematic Review of Tools for AI-Augmented Data Quality Management in Data Warehouses
FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation
RLSF: Fine-tuning LLMs via Symbolic Feedback
A Survey on Patent Analysis: From NLP to Multimodal AI
Enhancing Object Detection Robustness: Detecting and Restoring Confidence in the Presence of Adversarial Patch Attacks
Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought
Programming Distributed Collective Processes in the eXchange Calculus
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum
Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
On CNF formulas irredundant with respect to unit clause propagation
SONG: Self-Organizing Neural Graphs
Mobile-R1: Towards Interactive Reinforcement Learning for VLM-Based Mobile Agent via Task-Level Rewards
KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models
FEAT: A Preference Feedback Dataset through a Cost-Effective Auto-Generation and Labeling Framework for English AI Tutoring
Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team
PhysUniBench: An Undergraduate-Level Physics Reasoning Benchmark for Multimodal Models
From Human to Machine Psychology: A Conceptual Framework for Understanding Well-Being in Large Language Models
The SWE-Bench Illusion: When State-of-the-Art LLMs Remember Instead of Reason
VLM@school -- Evaluation of AI image understanding on German middle school knowledge
The AI Imperative: Scaling High-Quality Peer Review in Machine Learning
Toward Data Systems That Are Business Semantic Centric and AI Agents Assisted
ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows
$C^3$-Bench: The Things Real Disturbing LLM based Agent in Multi-Tasking
StarFT: Robust Fine-tuning of Zero-shot Models via Spuriosity Alignment
REMOR: Automated Peer Review Generation with LLM Reasoning and Multi-Objective Reinforcement Learning
Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'
Local Markov Equivalence and Local Causal Discovery for Identifying Controlled Direct Effects
Adapting Probabilistic Risk Assessment for AI
From Superficial to Deep: Integrating External Knowledge for Follow-up Question Generation Using Knowledge Graph and LLM
Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search
SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis
Problem Solving Through Human-AI Preference-Based Cooperation
CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents
CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings
HyperCLOVA X THINK Technical Report
Dehazing Light Microscopy Images with Guided Conditional Flow Matching: finding a sweet spot between fidelity and realism
QuickSilver -- Speeding up LLM Inference through Dynamic Token Halting, KV Skipping, Contextual Token Fusion, and Adaptive Matryoshka Quantization
Multi-View Contrastive Learning for Robust Domain Adaptation in Medical Time Series Analysis
Towards Distributed Neural Architectures
Can Video Large Multimodal Models Think Like Doubters-or Double-Down: A Study on Defeasible Video Entailment
Probabilistic Optimality for Inference-time Scaling
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications
Concept-Level AI for Telecom: Moving Beyond Large Language Models
A Framework for Multi-source Privacy Preserving Epidemic Analysis
A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake
Less Greedy Equivalence Search
A Practical Approach to Power Saving in Hearables Using Sub-Nyquist Sampling with Bandwidth Extension
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Explicit neural network classifiers for non-separable data
Created by
Haebom
저자
Patr
icia Mu
noz Ewald
개요
본 논문은 다층 퍼셉트론(feedforward neural network)의 광범위한 클래스를 절단 사상(truncation maps)을 이용하여 완전히 특징짓는다. ReLU 신경망이 동심원 데이터를 분리하는 특징 사상을 구현하는 방법을 응용 사례로 제시한다.
시사점, 한계점
•
시사점:
ReLU 신경망을 포함한 다양한 신경망의 기능을 절단 사상으로 분석하는 새로운 프레임워크를 제공한다. 동심원 데이터 분류와 같은 특정 문제에 대한 신경망 설계에 대한 통찰력을 제공한다.
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한계점:
제시된 프레임워크가 모든 유형의 신경망에 적용 가능한지는 불확실하다. 실제 데이터셋에 대한 실험적 검증이 부족하다. 절단 사상의 해석 가능성에 대한 추가적인 연구가 필요하다.
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