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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

[2602.21957] Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendation
Machine Learning

[2602.21957] Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendation

The paper presents CGFedRec, a novel framework for federated recommendation that enhances collaboration by using cluster-guided item alig...

arXiv - Machine Learning · 4 min ·
[2602.21926] Bridging Through Absence: How Comeback Researchers Bridge Knowledge Gaps Through Structural Re-emergence
Machine Learning

[2602.21926] Bridging Through Absence: How Comeback Researchers Bridge Knowledge Gaps Through Structural Re-emergence

This article explores the role of 'comeback researchers'—those who return to academia after a hiatus—in bridging knowledge gaps and enhan...

arXiv - Machine Learning · 4 min ·
[2602.21846] Scalable Kernel-Based Distances for Statistical Inference and Integration
Machine Learning

[2602.21846] Scalable Kernel-Based Distances for Statistical Inference and Integration

This paper explores scalable kernel-based distances for statistical inference, focusing on the maximum mean discrepancy (MMD) and introdu...

arXiv - Machine Learning · 4 min ·
[2602.21797] Neural Learning of Fast Matrix Multiplication Algorithms: A StrassenNet Approach
Machine Learning

[2602.21797] Neural Learning of Fast Matrix Multiplication Algorithms: A StrassenNet Approach

The paper presents StrassenNet, a neural architecture that learns fast matrix multiplication algorithms, specifically reproducing the Str...

arXiv - Machine Learning · 3 min ·
[2602.21788] DHP: Efficient Scaling of MLLM Training with Dynamic Hybrid Parallelism
Llms

[2602.21788] DHP: Efficient Scaling of MLLM Training with Dynamic Hybrid Parallelism

The paper presents Dynamic Hybrid Parallelism (DHP), a new strategy for efficiently scaling the training of Multimodal Large Language Mod...

arXiv - Machine Learning · 3 min ·
[2602.21766] RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms
Machine Learning

[2602.21766] RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms

The RAMSeS framework enhances time-series anomaly detection by combining a stacking ensemble with adaptive model selection, optimizing pe...

arXiv - Machine Learning · 3 min ·
[2602.21756] Offline Reasoning for Efficient Recommendation: LLM-Empowered Persona-Profiled Item Indexing
Llms

[2602.21756] Offline Reasoning for Efficient Recommendation: LLM-Empowered Persona-Profiled Item Indexing

The paper presents Persona4Rec, a novel recommendation framework that utilizes offline reasoning with large language models (LLMs) to cre...

arXiv - Machine Learning · 4 min ·
[2602.21721] Private and Robust Contribution Evaluation in Federated Learning
Machine Learning

[2602.21721] Private and Robust Contribution Evaluation in Federated Learning

This paper presents novel methods for evaluating contributions in federated learning while ensuring privacy and robustness, addressing vu...

arXiv - Machine Learning · 4 min ·
[2602.21707] Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries
Machine Learning

[2602.21707] Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries

This paper presents a novel approach to image reconstruction using spatially adaptive sparsity level maps within convolutional dictionari...

arXiv - Machine Learning · 4 min ·
[2602.21620] Revisiting the Bertrand Paradox via Equilibrium Analysis of No-regret Learners
Machine Learning

[2602.21620] Revisiting the Bertrand Paradox via Equilibrium Analysis of No-regret Learners

This article revisits the Bertrand Paradox using a theoretical framework that incorporates no-regret learning strategies in a discrete pr...

arXiv - Machine Learning · 3 min ·
[2602.21572] Goodness-of-Fit Tests for Latent Class Models with Ordinal Categorical Data
Machine Learning

[2602.21572] Goodness-of-Fit Tests for Latent Class Models with Ordinal Categorical Data

This article presents a new goodness-of-fit test for latent class models applied to ordinal categorical data, addressing the challenge of...

arXiv - Machine Learning · 3 min ·
[2602.21569] How many asymmetric communities are there in multi-layer directed networks?
Machine Learning

[2602.21569] How many asymmetric communities are there in multi-layer directed networks?

This paper explores the estimation of asymmetric community numbers in multi-layer directed networks, introducing a novel goodness-of-fit ...

arXiv - Machine Learning · 4 min ·
[2602.21533] Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework
Llms

[2602.21533] Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework

This paper presents the MAESTRO framework, which utilizes multi-agent large language models to discover high-performance single atom cata...

arXiv - Machine Learning · 3 min ·
[2602.21509] Fair Model-based Clustering
Machine Learning

[2602.21509] Fair Model-based Clustering

The paper presents Fair Model-based Clustering (FMC), a new algorithm that enhances fairness in clustering by ensuring the proportion of ...

arXiv - Machine Learning · 3 min ·
[2602.21501] A Researcher's Guide to Empirical Risk Minimization
Machine Learning

[2602.21501] A Researcher's Guide to Empirical Risk Minimization

This article provides a comprehensive guide on empirical risk minimization (ERM), detailing high-probability regret bounds and modular pr...

arXiv - Machine Learning · 4 min ·
[2602.21479] Global Sequential Testing for Multi-Stream Auditing
Machine Learning

[2602.21479] Global Sequential Testing for Multi-Stream Auditing

The paper presents a novel approach to global sequential testing for auditing machine learning systems across multiple data streams, enha...

arXiv - Machine Learning · 3 min ·
[2602.21478] Efficient Inference after Directionally Stable Adaptive Experiments
Machine Learning

[2602.21478] Efficient Inference after Directionally Stable Adaptive Experiments

This paper explores efficient inference methods for adaptive experiments, introducing the concept of directional stability, which enhance...

arXiv - Machine Learning · 3 min ·
[2602.21446] ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding
Machine Learning

[2602.21446] ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding

The paper presents ConformalHDC, a framework that integrates uncertainty quantification into hyperdimensional computing for improved neur...

arXiv - Machine Learning · 4 min ·
[2602.21436] Efficient Uncoupled Learning Dynamics with $\tilde{O}\!\left(T^{-1/4}\right)$ Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback
Machine Learning

[2602.21436] Efficient Uncoupled Learning Dynamics with $\tilde{O}\!\left(T^{-1/4}\right)$ Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback

This paper presents an efficient uncoupled learning algorithm for bilinear saddle-point problems, achieving last-iterate convergence with...

arXiv - Machine Learning · 3 min ·
[2602.21312] Precedence-Constrained Decision Trees and Coverings
Data Science

[2602.21312] Precedence-Constrained Decision Trees and Coverings

This paper explores optimization problems related to precedence-constrained decision trees and set coverings, presenting new approximatio...

arXiv - Machine Learning · 4 min ·
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