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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 ·
Llms

[R] Looking for arXiv cs.LG endorser, inference monitoring using information geometry

Hi r/MachineLearning, I’m looking for an arXiv endorser in cs.LG for a paper on inference-time distribution shift detection for deployed ...

Reddit - Machine Learning · 1 min ·
Llms

Nvidia goes all-in on AI agents while Anthropic pulls the plug

TLDR: Nvidia is partnering with 17 major companies to build a platform specifically for enterprise AI agents, basically trying to become ...

Reddit - Artificial Intelligence · 1 min ·

All Content

[2507.04517] DOTResize: Reducing LLM Width via Discrete Optimal Transport-based Neuron Merging
Llms

[2507.04517] DOTResize: Reducing LLM Width via Discrete Optimal Transport-based Neuron Merging

The paper presents DOTResize, a novel method for reducing the width of Large Language Models (LLMs) through Discrete Optimal Transport-ba...

arXiv - Machine Learning · 4 min ·
[2507.12652] Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspective
Machine Learning

[2507.12652] Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspective

This article explores the application of federated learning (FL) in offline and online EMG decoding, addressing privacy and performance c...

arXiv - Machine Learning · 4 min ·
[2506.21427] Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning
Machine Learning

[2506.21427] Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning

The paper presents the Single-Step Completion Policy (SSCP), a novel approach in reinforcement learning that enhances efficiency and expr...

arXiv - Machine Learning · 4 min ·
[2504.07835] Pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks
Machine Learning

[2504.07835] Pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks

The paper presents Pychop, a Python library that emulates low-precision arithmetic for numerical methods and neural networks, enhancing c...

arXiv - Machine Learning · 4 min ·
[2505.23725] MuLoCo: Muon is a practical inner optimizer for DiLoCo
Llms

[2505.23725] MuLoCo: Muon is a practical inner optimizer for DiLoCo

The paper presents MuLoCo, a new inner optimizer for the DiLoCo framework, demonstrating its superior performance in training large langu...

arXiv - Machine Learning · 4 min ·
[2502.00944] Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms
Machine Learning

[2502.00944] Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms

This article analyzes the impact of static and dynamic batching algorithms on training speed and performance in graph neural networks (GN...

arXiv - Machine Learning · 4 min ·
[2602.22056] FlowCorrect: Efficient Interactive Correction of Generative Flow Policies for Robotic Manipulation
Machine Learning

[2602.22056] FlowCorrect: Efficient Interactive Correction of Generative Flow Policies for Robotic Manipulation

The paper presents FlowCorrect, a framework for correcting generative flow policies in robotic manipulation using minimal human input, im...

arXiv - Machine Learning · 3 min ·
[2602.21873] GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task
Machine Learning

[2602.21873] GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task

The GFPL framework enhances federated learning by addressing data imbalance and communication overhead in resource-constrained vision tas...

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 ·
[2602.21788] 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 ·
[2602.21756] 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 ·
[2602.21478] 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 ·
[2602.21428] PSF-Med: Measuring and Explaining Paraphrase Sensitivity in Medical Vision Language Models
Llms

[2602.21428] PSF-Med: Measuring and Explaining Paraphrase Sensitivity in Medical Vision Language Models

The paper introduces PSF-Med, a benchmark assessing paraphrase sensitivity in medical vision language models, revealing significant varia...

arXiv - Machine Learning · 4 min ·
[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 ·
[2602.21265] ToolMATH: A Math Tool Benchmark for Realistic Long-Horizon Multi-Tool Reasoning
Llms

[2602.21265] ToolMATH: A Math Tool Benchmark for Realistic Long-Horizon Multi-Tool Reasoning

ToolMATH introduces a benchmark for evaluating tool-augmented language models in realistic multi-tool environments, focusing on long-hori...

arXiv - Machine Learning · 4 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 ·
[2602.22015] 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 ·
[2602.21965] 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 ·
[2602.21824] DocDjinn: Controllable Synthetic Document Generation with VLMs and Handwriting Diffusion
Llms

[2602.21824] DocDjinn: Controllable Synthetic Document Generation with VLMs and Handwriting Diffusion

DocDjinn introduces a framework for generating synthetic documents using Vision-Language Models (VLMs), addressing challenges in data acq...

arXiv - Machine Learning · 4 min ·
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Machine Learning Large Language Models Generative AI Natural Language Processing Computer Vision Robotics & Embodied AI

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