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Nomadic raises $8.4 million to wrangle the data pouring off autonomous vehicles | TechCrunch
Machine Learning

Nomadic raises $8.4 million to wrangle the data pouring off autonomous vehicles | TechCrunch

The company turns footage from robots into structured, searchable datasets with a deep learning model.

TechCrunch - AI · 6 min ·
Machine Learning

[R] VLMs Behavior for Long Video Understanding

I have extensively searched on long video understanding datasets such as Video-MME, MLVU, VideoBench, LongVideoBench and etc. What I have...

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 ·

All Content

[2602.22405] MolFM-Lite: Multi-Modal Molecular Property Prediction with Conformer Ensemble Attention and Cross-Modal Fusion
Machine Learning

[2602.22405] MolFM-Lite: Multi-Modal Molecular Property Prediction with Conformer Ensemble Attention and Cross-Modal Fusion

MolFM-Lite introduces a multi-modal approach to molecular property prediction, integrating various molecular representations through adva...

arXiv - Machine Learning · 4 min ·
[2602.22400] Predicting Multi-Drug Resistance in Bacterial Isolates Through Performance Comparison and LIME-based Interpretation of Classification Models
Machine Learning

[2602.22400] Predicting Multi-Drug Resistance in Bacterial Isolates Through Performance Comparison and LIME-based Interpretation of Classification Models

This study presents a machine learning framework to predict multi-drug resistance (MDR) in bacterial isolates, utilizing five classificat...

arXiv - Machine Learning · 4 min ·
[2602.22401] Vibe Researching as Wolf Coming: Can AI Agents with Skills Replace or Augment Social Scientists?
Robotics

[2602.22401] Vibe Researching as Wolf Coming: Can AI Agents with Skills Replace or Augment Social Scientists?

This paper explores the potential of AI agents to replace or augment social scientists by introducing the concept of 'vibe researching,' ...

arXiv - AI · 4 min ·
[2602.22387] Disentangling Shared and Target-Enriched Topics via Background-Contrastive Non-negative Matrix Factorization
Machine Learning

[2602.22387] Disentangling Shared and Target-Enriched Topics via Background-Contrastive Non-negative Matrix Factorization

This article introduces a novel method called background contrastive Non-negative Matrix Factorization, aimed at isolating biological sig...

arXiv - Machine Learning · 4 min ·
[2602.22287] Multi-Level Causal Embeddings
Machine Learning

[2602.22287] Multi-Level Causal Embeddings

This article presents a framework for Multi-Level Causal Embeddings, which allows for the mapping of detailed causal models into coarser ...

arXiv - Machine Learning · 3 min ·
[2602.22367] Learning geometry-dependent lead-field operators for forward ECG modeling
Machine Learning

[2602.22367] Learning geometry-dependent lead-field operators for forward ECG modeling

This article presents a novel approach to forward electrocardiogram (ECG) modeling using geometry-dependent lead-field operators, enhanci...

arXiv - AI · 4 min ·
[2602.22273] FIRE: A Comprehensive Benchmark for Financial Intelligence and Reasoning Evaluation
Llms

[2602.22273] FIRE: A Comprehensive Benchmark for Financial Intelligence and Reasoning Evaluation

The FIRE benchmark evaluates financial intelligence and reasoning in LLMs through diverse theoretical and practical assessments, providin...

arXiv - Machine Learning · 3 min ·
[2602.22334] A 1/R Law for Kurtosis Contrast in Balanced Mixtures
Machine Learning

[2602.22334] A 1/R Law for Kurtosis Contrast in Balanced Mixtures

This paper presents a new redundancy law for kurtosis contrast in balanced mixtures, demonstrating how effective width impacts kurtosis e...

arXiv - AI · 3 min ·
[2602.22297] Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection
Machine Learning

[2602.22297] Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection

This paper presents a novel approach to machinery fault detection using Adversarial Inverse Reinforcement Learning, enabling effective an...

arXiv - AI · 4 min ·
[2602.22298] AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts
Machine Learning

[2602.22298] AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts

AviaSafe introduces a physics-informed, data-driven model for aviation cloud forecasts, enhancing prediction accuracy for critical hydrom...

arXiv - AI · 3 min ·
[2602.22294] When Should a Model Change Its Mind? An Energy-Based Theory and Regularizer for Concept Drift in Electrocardiogram (ECG) Signals
Machine Learning

[2602.22294] When Should a Model Change Its Mind? An Energy-Based Theory and Regularizer for Concept Drift in Electrocardiogram (ECG) Signals

This paper presents an energy-based framework for managing concept drift in ECG signals, proposing a new regularizer that enhances model ...

arXiv - Machine Learning · 4 min ·
[2602.22293] Global River Forecasting with a Topology-Informed AI Foundation Model
Llms

[2602.22293] Global River Forecasting with a Topology-Informed AI Foundation Model

The paper presents GraphRiverCast (GRC), a topology-informed AI model designed for global river forecasting, enabling robust hydrodynamic...

arXiv - Machine Learning · 4 min ·
[2602.22288] Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy
Machine Learning

[2602.22288] Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy

This article discusses a novel explainable AI (XAI) method for predicting sudden cardiac death in Chagas cardiomyopathy, emphasizing its ...

arXiv - Machine Learning · 4 min ·
[2602.22286] OmniZip: Learning a Unified and Lightweight Lossless Compressor for Multi-Modal Data
Llms

[2602.22286] OmniZip: Learning a Unified and Lightweight Lossless Compressor for Multi-Modal Data

OmniZip introduces a unified and lightweight lossless compressor designed for multi-modal data, enhancing compression efficiency across v...

arXiv - Machine Learning · 4 min ·
[2602.22285] Early Risk Stratification of Dosing Errors in Clinical Trials Using Machine Learning
Machine Learning

[2602.22285] Early Risk Stratification of Dosing Errors in Clinical Trials Using Machine Learning

This study presents a machine learning framework for early risk stratification of dosing errors in clinical trials, utilizing pre-initiat...

arXiv - AI · 4 min ·
[2602.22280] Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion
Llms

[2602.22280] Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion

This research paper explores the integration of machine learning ensembles and large language models for predicting heart disease, demons...

arXiv - AI · 4 min ·
[2602.22274] Positional-aware Spatio-Temporal Network for Large-Scale Traffic Prediction
Machine Learning

[2602.22274] Positional-aware Spatio-Temporal Network for Large-Scale Traffic Prediction

The paper presents a Positional-aware Spatio-Temporal Network (PASTN) designed for large-scale traffic prediction, addressing the challen...

arXiv - AI · 3 min ·
[2602.22270] Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting
Machine Learning

[2602.22270] Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting

The paper presents a novel framework, STOEP, for spatio-temporal epidemic forecasting, addressing challenges in existing methods by integ...

arXiv - Machine Learning · 4 min ·
[2602.22267] Data-Driven Supervision of a Thermal-Hydraulic Process Towards a Physics-Based Digital Twin
Machine Learning

[2602.22267] Data-Driven Supervision of a Thermal-Hydraulic Process Towards a Physics-Based Digital Twin

This paper discusses the development of a digital twin for thermal-hydraulic processes, focusing on real-time supervision, fault detectio...

arXiv - Machine Learning · 4 min ·
[2602.22266] WaveSSM: Multiscale State-Space Models for Non-stationary Signal Attention
Machine Learning

[2602.22266] WaveSSM: Multiscale State-Space Models for Non-stationary Signal Attention

The paper introduces WaveSSM, a novel multiscale state-space model designed to enhance the modeling of non-stationary signals, outperform...

arXiv - Machine Learning · 3 min ·
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