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.

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 based 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

MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser

Created by

Haebom

저자

Armina Fani (Tri-Institutional Center for Translational Research in Neuroimaging and Data Science), Mike Doan (Tri-Institutional Center for Translational Research in Neuroimaging and Data Science), Isabelle Le (Tri-Institutional Center for Translational Research in Neuroimaging and Data Science), Alex Fedorov (Emory University), Malte Hoffmann (Harvard University), Chris Rorden (University of South Carolina), Sergey Plis (Tri-Institutional Center for Translational Research in Neuroimaging and Data Science)

개요

MindGrab은 다양한 모달리티의 두부 영상에서 부피 두개골 제거를 위한 매개변수 및 메모리 효율적인 심층 완전 합성곱 모델입니다. 확장된 합성곱의 스펙트럼 해석을 바탕으로 설계된 이 아키텍처는 모달리티에 독립적인 합성 데이터로만 학습되었습니다. SynthStrip 데이터셋에서 가져온 606개의 다모달 성인 뇌 스캔(T1, T2, DWI, MRA, PDw MRI, EPI, CT, PET)의 후향적 데이터셋에서 평가되었으며, Dice 점수를 사용하여 SynthStrip, ROBEX, BET와 성능을 비교했습니다. 모든 모달리티에서 평균 Dice 점수 95.9(표준 편차 1.6)를 달성하여 기존 방법(ROBEX: 89.1, 표준 편차 7.7, P < 0.05; BET: 85.2, 표준 편차 14.4, P < 0.05)을 유의미하게 능가했습니다. SynthStrip(96.5, 표준 편차 1.1, P=0.0352)과 비교했을 때, 거의 절반의 시나리오에서 동등하거나 우수한 성능을 제공했으며, 다른 경우에는 사소한 차이(<3% Dice)만 보였습니다. MindGrab은 SynthStrip보다 95% 적은 매개변수(146,237 대 2,566,561)를 사용했습니다. 이러한 효율성으로 인해 추론 속도가 최소 2배 빨라졌고, GPU 메모리 사용량이 50% 줄었으며, 고성능 GPU가 없는 시스템을 포함한 더 넓은 범위의 하드웨어에서 탁월한 성능(예: 10-30배 속도 향상, 최대 30배 메모리 감소)과 접근성을 제공했습니다. MindGrab은 brainchop-cli (https://pypi.org/project/brainchop/) 및 brainchop.org에서 지원되는 극적으로 낮은 리소스 요구 사항으로 최첨단 정확도를 제공합니다.

Client Challenge

시사점, 한계점

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시사점:

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다양한 모달리티의 두부 영상에서 효율적이고 정확한 부피 두개골 제거를 가능하게 합니다.

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기존 방법보다 우수한 성능을 보이며, SynthStrip과 비슷하거나 더 나은 성능을 제공합니다.

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적은 매개변수와 메모리 사용량으로 인해 다양한 하드웨어에서 사용 가능하며, 추론 속도가 향상됩니다.

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brainchop-cli 및 brainchop.org를 통해 접근성이 높습니다.

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한계점:

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SynthStrip과 비교하여 일부 시나리오에서 약간의 성능 차이(3% 미만)가 존재합니다.

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합성 데이터로만 학습되었으므로 실제 데이터에 대한 일반화 성능에 대한 추가적인 연구가 필요할 수 있습니다.

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논문에서 구체적인 한계점이나 향후 연구 방향에 대한 언급이 부족합니다.

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