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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

[2602.13351] A Formal Framework for the Explanation of Finite Automata Decisions
Machine Learning

[2602.13351] A Formal Framework for the Explanation of Finite Automata Decisions

This paper presents a formal framework for explaining the decisions made by finite automata (FA), focusing on minimal input character set...

arXiv - AI · 4 min ·
[2602.13350] Detecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data
Machine Learning

[2602.13350] Detecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data

This paper presents a novel approach to detecting brick kiln infrastructure using high-resolution satellite imagery, focusing on a new mo...

arXiv - AI · 4 min ·
[2602.14687] SynthSAEBench: Evaluating Sparse Autoencoders on Scalable Realistic Synthetic Data
Llms

[2602.14687] SynthSAEBench: Evaluating Sparse Autoencoders on Scalable Realistic Synthetic Data

The paper introduces SynthSAEBench, a toolkit for evaluating Sparse Autoencoders (SAEs) using large-scale synthetic data, addressing limi...

arXiv - AI · 3 min ·
[2602.14663] Pseudo-differential-enhanced physics-informed neural networks
Machine Learning

[2602.14663] Pseudo-differential-enhanced physics-informed neural networks

This article introduces pseudo-differential-enhanced physics-informed neural networks (PINNs), which improve training efficiency and accu...

arXiv - Machine Learning · 4 min ·
[2602.13346] CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis
Llms

[2602.13346] CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis

CellMaster introduces an AI-driven approach for zero-shot cell-type annotation in single-cell RNA sequencing, improving accuracy signific...

arXiv - AI · 3 min ·
[2602.14656] An Embarrassingly Simple Way to Optimize Orthogonal Matrices at Scale
Machine Learning

[2602.14656] An Embarrassingly Simple Way to Optimize Orthogonal Matrices at Scale

This paper presents POGO, a novel algorithm for optimizing orthogonal matrices efficiently, addressing scalability issues in machine lear...

arXiv - Machine Learning · 3 min ·
[2602.13339] An Integrated Causal Inference Framework for Traffic Safety Modeling with Semantic Street-View Visual Features
Machine Learning

[2602.13339] An Integrated Causal Inference Framework for Traffic Safety Modeling with Semantic Street-View Visual Features

This article presents a novel causal inference framework for traffic safety modeling, utilizing semantic features from street-view images...

arXiv - AI · 4 min ·
[2602.13332] MedScope: Incentivizing "Think with Videos" for Clinical Reasoning via Coarse-to-Fine Tool Calling
Llms

[2602.13332] MedScope: Incentivizing "Think with Videos" for Clinical Reasoning via Coarse-to-Fine Tool Calling

The paper presents MedScope, a clinical video reasoning model that enhances decision-making in medical contexts by integrating tool use a...

arXiv - AI · 4 min ·
[2602.14602] OPBench: A Graph Benchmark to Combat the Opioid Crisis
Machine Learning

[2602.14602] OPBench: A Graph Benchmark to Combat the Opioid Crisis

OPBench introduces a comprehensive benchmark for evaluating graph learning methods aimed at addressing the opioid crisis, featuring five ...

arXiv - AI · 4 min ·
[2602.14578] RNM-TD3: N:M Semi-structured Sparse Reinforcement Learning From Scratch
Machine Learning

[2602.14578] RNM-TD3: N:M Semi-structured Sparse Reinforcement Learning From Scratch

The paper presents RNM-TD3, a novel approach to reinforcement learning that employs N:M structured sparsity, enhancing performance while ...

arXiv - Machine Learning · 3 min ·
[2602.14571] DCTracks: An Open Dataset for Machine Learning-Based Drift Chamber Track Reconstruction
Machine Learning

[2602.14571] DCTracks: An Open Dataset for Machine Learning-Based Drift Chamber Track Reconstruction

The article presents DCTracks, a new open dataset designed for machine learning-based track reconstruction in drift chambers, featuring s...

arXiv - Machine Learning · 3 min ·
[2602.13314] Sim2Radar: Toward Bridging the Radar Sim-to-Real Gap with VLM-Guided Scene Reconstruction
Machine Learning

[2602.13314] Sim2Radar: Toward Bridging the Radar Sim-to-Real Gap with VLM-Guided Scene Reconstruction

The paper presents Sim2Radar, a framework that generates synthetic radar data from RGB images, addressing the challenges of limited radar...

arXiv - AI · 3 min ·
[2602.13313] Agentic Spatio-Temporal Grounding via Collaborative Reasoning
Ai Agents

[2602.13313] Agentic Spatio-Temporal Grounding via Collaborative Reasoning

The paper presents the Agentic Spatio-Temporal Grounder (ASTG), a novel framework for Spatio-Temporal Video Grounding (STVG) that enhance...

arXiv - AI · 3 min ·
[2602.13312] PeroMAS: A Multi-agent System of Perovskite Material Discovery
Machine Learning

[2602.13312] PeroMAS: A Multi-agent System of Perovskite Material Discovery

PeroMAS introduces a multi-agent system for discovering perovskite materials, enhancing efficiency in photovoltaic research through a com...

arXiv - AI · 4 min ·
[2602.14519] DeepMTL2R: A Library for Deep Multi-task Learning to Rank
Machine Learning

[2602.14519] DeepMTL2R: A Library for Deep Multi-task Learning to Rank

DeepMTL2R is an open-source library designed for deep multi-task learning to rank, integrating diverse relevance signals into a unified m...

arXiv - Machine Learning · 3 min ·
[2602.14506] Covariance-Aware Transformers for Quadratic Programming and Decision Making
Machine Learning

[2602.14506] Covariance-Aware Transformers for Quadratic Programming and Decision Making

This paper introduces Covariance-Aware Transformers, a novel approach for solving quadratic programming (QP) problems, enhancing decision...

arXiv - Machine Learning · 4 min ·
[2602.14495] Divine Benevolence is an $x^2$: GLUs scale asymptotically faster than MLPs
Llms

[2602.14495] Divine Benevolence is an $x^2$: GLUs scale asymptotically faster than MLPs

This paper explores the scaling laws of Gated Linear Units (GLUs) compared to Multi-Layer Perceptrons (MLPs), demonstrating that GLUs sca...

arXiv - Machine Learning · 4 min ·
[2602.13305] WildfireVLM: AI-powered Analysis for Early Wildfire Detection and Risk Assessment Using Satellite Imagery
Computer Vision

[2602.13305] WildfireVLM: AI-powered Analysis for Early Wildfire Detection and Risk Assessment Using Satellite Imagery

WildfireVLM introduces an AI framework for early wildfire detection and risk assessment using satellite imagery, enhancing disaster manag...

arXiv - AI · 4 min ·
[2602.14474] One Good Source is All You Need: Near-Optimal Regret for Bandits under Heterogeneous Noise
Machine Learning

[2602.14474] One Good Source is All You Need: Near-Optimal Regret for Bandits under Heterogeneous Noise

The paper presents a novel algorithm, SOAR, for the K-armed Multiarmed Bandit problem that minimizes regret under heterogeneous noise, ac...

arXiv - Machine Learning · 4 min ·
[2602.13299] KidMesh: Computational Mesh Reconstruction for Pediatric Congenital Hydronephrosis Using Deep Neural Networks
Machine Learning

[2602.13299] KidMesh: Computational Mesh Reconstruction for Pediatric Congenital Hydronephrosis Using Deep Neural Networks

The paper presents KidMesh, a deep learning approach for reconstructing computational meshes for pediatric congenital hydronephrosis from...

arXiv - AI · 4 min ·
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