/
/
Daily Arxiv
Daily Arxiv
世界中で発行される人工知能関連の論文をまとめるページです。
このページはGoogle Geminiを活用して要約し、非営利で運営しています。
論文の著作権は著者および関連機関にあり、共有する際は出典を明記してください。
Dehazing Light Microscopy Images with Guided Conditional Flow Matching: finding a sweet spot between fidelity and realism
EFRame: Deeper Reasoning via Exploration-Filter-Replay Reinforcement Learning Framework
Refine-POI: Reinforcement Fine-Tuned Large Language Models for Next Point-of-Interest Recommendation
HalluSegBench: Counterfactual Visual Reasoning for Segmentation Hallucination Evaluation
Potemkin Understanding in Large Language Models
OmniEval: A Benchmark for Evaluating Omni-modal Models with Visual, Auditory, and Textual Inputs
How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models?
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
Arabic Dialect Classification using RNNs, Transformers, and Large Language Models: A Comparative Analysis
Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education
GLIMPSE: Gradient-Layer Importance Mapping for Prompted Visual Saliency Explanation for Generative LVLMs
Automatic Depression Assessment using Machine Learning: A Comprehensive Survey
Generalizing vision-language models to novel domains: A comprehensive survey
Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance
AI-Generated Song Detection via Lyrics Transcripts
KAG-Thinker: Interactive Thinking and Deep Reasoning in LLMs via Knowledge-Augmented Generation
Data Quality Issues in Multilingual Speech Datasets: The Need for Sociolinguistic Awareness and Proactive Language Planning
Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion
Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs
Value-Free Policy Optimization via Reward Partitioning
VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption
Enabling Precise Topic Alignment in Large Language Models Via Sparse Autoencoders
Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And Normalization
CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following
StepProof: Step-by-step verification of natural language mathematical proofs
Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment
Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting
SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving
FZOO: Fast Zeroth-Order Optimizer for Fine-Tuning Large Language Models towards Adam-Scale Speed
VeriLoC: Line-of-Code Level Prediction of Hardware Design Quality from Verilog Code
Multi Layered Autonomy and AI Ecologies in Robotic Art Installations
Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison
Quantum computing and artificial intelligence: status and perspectives
Fine-Tuning Next-Scale Visual Autoregressive Models with Group Relative Policy Optimization
A Large Language Model-Enabled Control Architecture for Dynamic Resource Capability Exploration in Multi-Agent Manufacturing Systems
Spotlight-TTS: Spotlighting the Style via Voiced-Aware Style Extraction and Style Direction Adjustment for Expressive Text-to-Speech
WeatherEdit: Controllable Weather Editing with 4D Gaussian Field
From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data
Error Optimization: Overcoming Exponential Signal Decay in Deep Predictive Coding Networks
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks
Super-Resolution Generative Adversarial Networks based Video Enhancement
Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix
INSIGHT: Bridging the Student-Teacher Gap in Times of Large Language Models
SConU: Selective Conformal Uncertainty in Large Language Models
MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation
Sculpting Memory: Multi-Concept Forgetting in Diffusion Models via Dynamic Mask and Concept-Aware Optimization
Achieving binary weight and activation for LLMs using Post-Training Quantization
A Consequentialist Critique of Binary Classification Evaluation Practices
Redefining Evaluation Standards: A Unified Framework for Evaluating the Korean Capabilities of Language Models
Test-Time Reasoning Through Visual Human Preferences with VLMs and Soft Rewards
FedMM-X: A Trustworthy and Interpretable Framework for Federated Multi-Modal Learning in Dynamic Environments
Automating Adjudication of Cardiovascular Events Using Large Language Models
ATTENTION2D: Communication Efficient Distributed Self-Attention Mechanism
Visual Position Prompt for MLLM ベースの Visual Grounding
Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding
Privacy Ethics Alignment in AI: A Stakeholder-Centric Framework for Ethical AI
Characterizing GPU Resilience and Impact on AI/HPC Systems
Explainable Sentiment Analysis with DeepSeek-R1: Performance, Efficiency, and Few-Shot Learning
Neurons: Emulating the Human Visual Cortex Improves Fidelity and Interpretability in fMRI-to-Video Reconstruction
The Problem of the Priors, or Posteriors?
Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding
Disrupting Model Merging: A Parameter-Level Defense Without Sacrificing Accuracy
What can large language models do for sustainable food?
Enough Coin Flips Can Make LLMs Act Bayesian
How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects
Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement
PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization
Space-Time Graphs of Convex Sets for Multi-Robot Motion Planning
HalCECE: A Framework for Explainable Hallucination Detection through Conceptual Counterfactuals in Image Captioning
LNUCB-TA: Linear-nonlinear Hybrid Bandit Learning with Temporal Attention
No, of course I can! Refusal Mechanisms Can Be Exploited Using Harmless Fine-Tuning Data
Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models
Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning
A general language model for peptide identification
Cluster and Predict Latent Patches for Improved Masked Image Modeling
Semantic-Aware Adaptive Video Streaming Using Latent Diffusion Models for Wireless Networks
KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy
Mechanistic Interpretability of Emotion Inference in Large Language Models
Multimodal Medical Code Tokenizer
Time to Rethink AI for Combinatorial Optimization: Classical Algorithms Remain Tough to Match
Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
Environment-Driven Online LiDAR-Camera Extrinsic Calibration
Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation
DReSS: Data-driven Regularized Structured Streamlining for Large Language Models
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection
Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models
DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection
An Investigation into Seasonal Variations in Energy Forecasting for Student Residences
Efficiently Serving Large Multimodal Models Using EPD Disaggregation
PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models
AlignGuard: Scalable Safety Alignment for Text-to-Image Generation
A Library for Learning Neural Operators
ZipAR: Parallel Auto-regressive Image Generation through Spatial Locality
Pretrained Reversible Generation as Unsupervised Visual Representation Learning
FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait
SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs?
Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters
Foundation Models for Wearable Movement Data in Mental Health Research
GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs
Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements
Load more
A Consequentialist Critique of Binary Classification Evaluation Practices
Created by
Haebom
作者
Gerardo Flores, Abigail Schiff, Alyssa H. Smith, Julia A Fukuyama, Ashia C. Wilson
概要
本論文は、機械学習ベースのバイナリ分類予測（例えば、検査指示、予防的拘禁決定）評価に関する既存の研究の問題を指摘し、意思決定理論の観点からより適切な評価方法を提案します。従来の研究では、精度、Precision@Kなどの指標やAUC-ROCなどのしきい値とは無関係の指標を使用する傾向がありましたが、論文では、さまざまなしきい値を考慮するBrier scoreやLog lossなどの指標がより適切であると主張しています。実際の主要協会（ICML、FAccT、CHIL）論文分析を通じてこれらの主張を裏付け、Brierスコアを活用するためのPythonパッケージbriertoolsを紹介します。さらに、Brier ScoreとDecision Curve Analysisの間の新しい理論的関連性を明らかにし、既存の proper scoring rule に対する批判（Assel et al., 2017）への回答を提示します。
Takeaways、Limitations
•
Takeaways:
◦
機械学習ベースの意思決定評価における意思決定理論的観点の重要性を強調し、Brier scoreやLog lossなどの指標の活用を提案します。
◦
Brier Score と Decision Curve Analysis の理論的関連性を解明し、従来のスコアリングルールに対する既存の批判に対する解決策を提示します。
◦
Brier scoreを活用するためのPythonパッケージbriertoolsを提供し、実際のアプリケーションを容易にします。
•
Limitations：
◦
主な学会論文分析の結果は特定の学会に限定され、一般化の制約がある可能性があります。
◦
提案されたBrier scoreとLog lossの活用が、すべての機械学習ベースの意思決定問題に常に最適であるという保証はありません。問題の性質によっては、他の指標がより適している可能性があります。
◦
Briertoolsパッケージのパフォーマンスとユーザーフレンドリー性をさらに検証する必要があるかもしれません。
PDFを見る
Made with Slashpage
