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Machine Learning

What image/video training data is hardest to find right now? [R]

I'm building a crowdsourced photo collection platform (contributors take photos with smartphones, we auto-label with YOLO/CLIP + enrich w...

Reddit - Machine Learning · 1 min ·
UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

AI News - General · 4 min ·
Accelerating science with AI and simulations
Machine Learning

Accelerating science with AI and simulations

MIT Professor Rafael Gómez-Bombarelli discusses the transformative potential of AI in scientific research, emphasizing its role in materi...

AI News - General · 10 min ·

All Content

[2602.13930] MamaDino: A Hybrid Vision Model for Breast Cancer 3-Year Risk Prediction
Machine Learning

[2602.13930] MamaDino: A Hybrid Vision Model for Breast Cancer 3-Year Risk Prediction

MamaDino is a novel hybrid vision model that enhances breast cancer risk prediction by utilizing lower-resolution mammograms while mainta...

arXiv - Machine Learning · 4 min ·
[2602.13906] Quantifying Normality: Convergence Rate to Gaussian Limit for Stochastic Approximation and Unadjusted OU Algorithm
Machine Learning

[2602.13906] Quantifying Normality: Convergence Rate to Gaussian Limit for Stochastic Approximation and Unadjusted OU Algorithm

This paper analyzes the convergence rate to a Gaussian limit for stochastic approximation methods, providing explicit non-asymptotic boun...

arXiv - Machine Learning · 4 min ·
[2602.13889] Parameter-Efficient Fine-Tuning of DINOv2 for Large-Scale Font Classification
Machine Learning

[2602.13889] Parameter-Efficient Fine-Tuning of DINOv2 for Large-Scale Font Classification

The paper presents a novel approach to font classification using DINOv2, achieving high accuracy with minimal parameter tuning and introd...

arXiv - Machine Learning · 3 min ·
[2602.14043] Beyond Static Snapshots: Dynamic Modeling and Forecasting of Group-Level Value Evolution with Large Language Models
Llms

[2602.14043] Beyond Static Snapshots: Dynamic Modeling and Forecasting of Group-Level Value Evolution with Large Language Models

This article presents a novel framework for dynamic modeling and forecasting of group-level value evolution using large language models (...

arXiv - AI · 4 min ·
[2602.13864] Evolving Multi-Channel Confidence-Aware Activation Functions for Missing Data with Channel Propagation
Machine Learning

[2602.13864] Evolving Multi-Channel Confidence-Aware Activation Functions for Missing Data with Channel Propagation

This paper presents a novel approach to activation functions in neural networks that incorporates missing data and confidence scores, enh...

arXiv - Machine Learning · 4 min ·
[2602.13847] Causally constrained reduced-order neural models of complex turbulent dynamical systems
Machine Learning

[2602.13847] Causally constrained reduced-order neural models of complex turbulent dynamical systems

This paper presents a novel framework for developing reduced-order neural models that accurately capture complex turbulent dynamical syst...

arXiv - Machine Learning · 3 min ·
[2602.14042] Restoration Adaptation for Semantic Segmentation on Low Quality Images
Machine Learning

[2602.14042] Restoration Adaptation for Semantic Segmentation on Low Quality Images

This paper presents a novel approach, Restoration Adaptation for Semantic Segmentation (RASS), which enhances semantic segmentation perfo...

arXiv - AI · 4 min ·
[2602.13811] A Unified Physics-Informed Neural Network for Modeling Coupled Electro- and Elastodynamic Wave Propagation Using Three-Stage Loss Optimization
Machine Learning

[2602.13811] A Unified Physics-Informed Neural Network for Modeling Coupled Electro- and Elastodynamic Wave Propagation Using Three-Stage Loss Optimization

This article presents a Physics-Informed Neural Network (PINN) model for simulating coupled electro- and elastodynamic wave propagation, ...

arXiv - Machine Learning · 3 min ·
[2602.14010] A Deployment-Friendly Foundational Framework for Efficient Computational Pathology
Llms

[2602.14010] A Deployment-Friendly Foundational Framework for Efficient Computational Pathology

This paper presents LitePath, a foundational framework for computational pathology that significantly reduces computational costs while m...

arXiv - AI · 4 min ·
[2602.14009] Named Entity Recognition for Payment Data Using NLP
Machine Learning

[2602.14009] Named Entity Recognition for Payment Data Using NLP

This paper explores Named Entity Recognition (NER) techniques for payment data, presenting advanced models like PaymentBERT that enhance ...

arXiv - AI · 3 min ·
[2602.13770] NeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoning
Llms

[2602.13770] NeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoning

The paper presents NeuroMambaLLM, an innovative framework that integrates dynamic graph learning and language model reasoning to analyze ...

arXiv - Machine Learning · 4 min ·
[2602.13971] DAIAN: Deep Adaptive Intent-Aware Network for CTR Prediction in Trigger-Induced Recommendation
Machine Learning

[2602.13971] DAIAN: Deep Adaptive Intent-Aware Network for CTR Prediction in Trigger-Induced Recommendation

The paper presents DAIAN, a Deep Adaptive Intent-Aware Network designed to enhance Click-Through Rate (CTR) prediction in Trigger-Induced...

arXiv - AI · 4 min ·
[2602.13672] LEAD-Drift: Real-time and Explainable Intent Drift Detection by Learning a Data-Driven Risk Score
Machine Learning

[2602.13672] LEAD-Drift: Real-time and Explainable Intent Drift Detection by Learning a Data-Driven Risk Score

The LEAD-Drift framework offers a real-time solution for detecting intent drift in Intent-Based Networking (IBN), enhancing proactive net...

arXiv - Machine Learning · 4 min ·
[2602.13619] Locally Private Parametric Methods for Change-Point Detection
Ai Startups

[2602.13619] Locally Private Parametric Methods for Change-Point Detection

This paper presents novel locally private parametric methods for change-point detection, focusing on maintaining privacy while identifyin...

arXiv - Machine Learning · 3 min ·
[2602.13920] A Comparative Analysis of Social Network Topology in Reddit and Moltbook
Ai Agents

[2602.13920] A Comparative Analysis of Social Network Topology in Reddit and Moltbook

This paper presents a comparative analysis of social network topology between Reddit and Moltbook, an AI-driven platform, highlighting ke...

arXiv - AI · 3 min ·
[2602.13513] Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization
Machine Learning

[2602.13513] Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization

This paper explores data-driven equation discovery to enhance optimization processes in engineering, introducing the Learned Gradient Flo...

arXiv - Machine Learning · 4 min ·
[2602.13851] Evaluating LLM-Generated ACSL Annotations for Formal Verification
Llms

[2602.13851] Evaluating LLM-Generated ACSL Annotations for Formal Verification

This paper evaluates the effectiveness of LLM-generated ACSL annotations for formal verification in C programs, comparing multiple genera...

arXiv - AI · 3 min ·
[2602.13842] Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation
Machine Learning

[2602.13842] Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation

This paper explores the use of deep learning to predict paravalvular regurgitation (PVR) in patients undergoing Transcatheter Aortic Valv...

arXiv - AI · 4 min ·
[2602.13414] FUTON: Fourier Tensor Network for Implicit Neural Representations
Machine Learning

[2602.13414] FUTON: Fourier Tensor Network for Implicit Neural Representations

The paper introduces FUTON, a Fourier Tensor Network designed to enhance implicit neural representations (INRs) by improving convergence ...

arXiv - Machine Learning · 3 min ·
[2602.13812] DTBench: A Synthetic Benchmark for Document-to-Table Extraction
Llms

[2602.13812] DTBench: A Synthetic Benchmark for Document-to-Table Extraction

DTBench introduces a synthetic benchmark for evaluating document-to-table extraction capabilities, addressing limitations in existing ben...

arXiv - AI · 4 min ·
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