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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

[2602.21357] Conditional neural control variates for variance reduction in Bayesian inverse problems
Machine Learning

[2602.21357] Conditional neural control variates for variance reduction in Bayesian inverse problems

This paper presents a novel approach using conditional neural control variates to reduce variance in Bayesian inverse problems, enhancing...

arXiv - Machine Learning · 4 min ·
[2602.21272] Counterdiabatic Hamiltonian Monte Carlo
Ai Safety

[2602.21272] Counterdiabatic Hamiltonian Monte Carlo

The paper introduces Counterdiabatic Hamiltonian Monte Carlo (CHMC), an advanced sampling method that improves the efficiency of Hamilton...

arXiv - Machine Learning · 3 min ·
[2602.21252] INTACT: Intent-Aware Representation Learning for Cryptographic Traffic Violation Detection
Machine Learning

[2602.21252] INTACT: Intent-Aware Representation Learning for Cryptographic Traffic Violation Detection

The paper introduces INTACT, a novel framework for detecting cryptographic traffic violations by modeling violations as conditional const...

arXiv - Machine Learning · 3 min ·
[2602.22136] SigmaQuant: Hardware-Aware Heterogeneous Quantization Method for Edge DNN Inference
Machine Learning

[2602.22136] SigmaQuant: Hardware-Aware Heterogeneous Quantization Method for Edge DNN Inference

The paper introduces SigmaQuant, a hardware-aware heterogeneous quantization method for deep neural networks (DNNs) aimed at optimizing p...

arXiv - Machine Learning · 3 min ·
[2602.20376] Exploiting Low-Rank Structure in Max-K-Cut Problems
Data Science

[2602.20376] Exploiting Low-Rank Structure in Max-K-Cut Problems

The paper explores the Max-3-Cut problem by leveraging low-rank structures in complex-valued quadratic forms, proposing new algorithms th...

arXiv - Machine Learning · 3 min ·
[2602.22179] Learning and Naming Subgroups with Exceptional Survival Characteristics
Machine Learning

[2602.22179] Learning and Naming Subgroups with Exceptional Survival Characteristics

The paper presents Sysurv, a novel non-parametric method for identifying subpopulations with exceptional survival characteristics, enhanc...

arXiv - Machine Learning · 3 min ·
[2602.22130] Sample Complexity Bounds for Robust Mean Estimation with Mean-Shift Contamination
Machine Learning

[2602.22130] Sample Complexity Bounds for Robust Mean Estimation with Mean-Shift Contamination

This paper addresses the sample complexity of robust mean estimation under mean-shift contamination, providing new algorithms and bounds ...

arXiv - Machine Learning · 4 min ·
[2602.22018] Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data
Machine Learning

[2602.22018] Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data

This article presents a novel disease progression model, Mixed-SuStaIn, which integrates both discrete and continuous data types to impro...

arXiv - Machine Learning · 4 min ·
[2602.22015] Function-Space Empirical Bayes Regularisation with Student's t Priors
Machine Learning

[2602.22015] Function-Space Empirical Bayes Regularisation with Student's t Priors

This paper presents a novel function-space empirical Bayes regularisation framework using heavy-tailed Student's t priors to improve Baye...

arXiv - Machine Learning · 3 min ·
[2602.22003] Neural solver for Wasserstein Geodesics and optimal transport dynamics
Machine Learning

[2602.22003] Neural solver for Wasserstein Geodesics and optimal transport dynamics

This article presents a novel neural solver for computing Wasserstein geodesics and optimal transport dynamics, enhancing the modeling of...

arXiv - Machine Learning · 3 min ·
[2602.21965] Compact Circulant Layers with Spectral Priors
Machine Learning

[2602.21965] Compact Circulant Layers with Spectral Priors

This paper explores compact circulant layers with spectral priors, focusing on their application in memory-efficient neural networks for ...

arXiv - Machine Learning · 3 min ·
[2602.21961] Robustness in sparse artificial neural networks trained with adaptive topology
Machine Learning

[2602.21961] Robustness in sparse artificial neural networks trained with adaptive topology

This paper explores the robustness of sparse artificial neural networks with adaptive topology, demonstrating their competitive performan...

arXiv - Machine Learning · 3 min ·
[2602.21959] Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions
Machine Learning

[2602.21959] Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions

This article reviews methods for estimating and optimizing ship fuel consumption, addressing challenges and proposing future research dir...

arXiv - Machine Learning · 4 min ·
[2602.21948] Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis
Machine Learning

[2602.21948] Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis

This paper introduces GACTGAN, a Bayesian Generative Adversarial Network that utilizes Gaussian approximation for synthesizing tabular da...

arXiv - Machine Learning · 3 min ·
[2602.21919] Learning in the Null Space: Small Singular Values for Continual Learning
Machine Learning

[2602.21919] Learning in the Null Space: Small Singular Values for Continual Learning

The paper presents NESS, a novel continual learning method that leverages small singular values to maintain orthogonality in weight updat...

arXiv - Machine Learning · 4 min ·
[2602.21928] Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systems
Machine Learning

[2602.21928] Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systems

This paper presents a novel federated learning methodology for decentralized root cause analysis in nonlinear dynamical systems, addressi...

arXiv - Machine Learning · 4 min ·
[2602.21910] The Error of Deep Operator Networks Is the Sum of Its Parts: Branch-Trunk and Mode Error Decompositions
Machine Learning

[2602.21910] The Error of Deep Operator Networks Is the Sum of Its Parts: Branch-Trunk and Mode Error Decompositions

This paper analyzes the performance limitations of Deep Operator Networks (DeepONets) and proposes modifications to improve their accurac...

arXiv - Machine Learning · 4 min ·
[2602.21844] JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning
Machine Learning

[2602.21844] JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning

The paper presents JSAM, a framework for optimizing client selection and privacy compensation in differentially private federated learnin...

arXiv - Machine Learning · 4 min ·
[2602.21773] Easy to Learn, Yet Hard to Forget: Towards Robust Unlearning Under Bias
Machine Learning

[2602.21773] Easy to Learn, Yet Hard to Forget: Towards Robust Unlearning Under Bias

This paper discusses the challenges of machine unlearning in the presence of biased data, introducing a novel framework called CUPID to e...

arXiv - Machine Learning · 4 min ·
[2602.21717] C$^{2}$TC: A Training-Free Framework for Efficient Tabular Data Condensation
Machine Learning

[2602.21717] C$^{2}$TC: A Training-Free Framework for Efficient Tabular Data Condensation

C$^{2}$TC introduces a training-free framework for efficient tabular data condensation, addressing challenges in data scalability and mod...

arXiv - Machine Learning · 4 min ·
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