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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 ·
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 ·
[2512.24420] Virasoro Symmetry in Neural Network Field Theories
Machine Learning

[2512.24420] Virasoro Symmetry in Neural Network Field Theories

Abstract page for arXiv paper 2512.24420: Virasoro Symmetry in Neural Network Field Theories

arXiv - Machine Learning · 3 min ·

All Content

[2512.02435] Efficient Cross-Domain Offline Reinforcement Learning with Dynamics- and Value-Aligned Data Filtering
Nlp

[2512.02435] Efficient Cross-Domain Offline Reinforcement Learning with Dynamics- and Value-Aligned Data Filtering

This paper presents a novel framework for cross-domain offline reinforcement learning, introducing a method that filters data based on bo...

arXiv - Machine Learning · 4 min ·
[2510.10625] ImpMIA: Leveraging Implicit Bias for Membership Inference Attack
Machine Learning

[2510.10625] ImpMIA: Leveraging Implicit Bias for Membership Inference Attack

The paper introduces ImpMIA, a novel Membership Inference Attack that leverages implicit bias in neural networks to identify training sam...

arXiv - Machine Learning · 4 min ·
[2510.04091] Rethinking Consistent Multi-Label Classification Under Inexact Supervision
Machine Learning

[2510.04091] Rethinking Consistent Multi-Label Classification Under Inexact Supervision

This paper presents a novel approach to multi-label classification under inexact supervision, addressing the limitations of existing meth...

arXiv - Machine Learning · 4 min ·
[2510.02823] The Curious Case of In-Training Compression of State Space Models
Machine Learning

[2510.02823] The Curious Case of In-Training Compression of State Space Models

This paper explores in-training compression techniques for State Space Models (SSMs), demonstrating how selective dimension preservation ...

arXiv - Machine Learning · 4 min ·
[2509.25800] Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data
Nlp

[2509.25800] Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data

This paper presents a novel approach to causal discovery that accounts for latent confounders and post-treatment selection, enhancing the...

arXiv - Machine Learning · 4 min ·
[2510.01988] PepCompass: Navigating peptide embedding spaces using Riemannian Geometry
Machine Learning

[2510.01988] PepCompass: Navigating peptide embedding spaces using Riemannian Geometry

PepCompass introduces a geometry-aware framework for exploring peptide spaces, enhancing antimicrobial peptide discovery through advanced...

arXiv - Machine Learning · 4 min ·
[2508.09888] Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?
Machine Learning

[2508.09888] Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?

This article presents a comprehensive evaluation of modern neural networks for small tabular datasets in the context of digital soil mapp...

arXiv - Machine Learning · 4 min ·
[2508.04605] Multitask Learning with Stochastic Interpolants
Machine Learning

[2508.04605] Multitask Learning with Stochastic Interpolants

This article presents a new framework for multitask learning using stochastic interpolants, enhancing generative models' capabilities acr...

arXiv - Machine Learning · 3 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 ·
[2507.00031] Enhancing Spatio-Temporal Forecasting with Spatial Neighbourhood Fusion:A Case Study on COVID-19 Mobility in Peru
Machine Learning

[2507.00031] Enhancing Spatio-Temporal Forecasting with Spatial Neighbourhood Fusion:A Case Study on COVID-19 Mobility in Peru

This paper presents a novel Spatial Neighbourhood Fusion technique to enhance spatio-temporal forecasting of COVID-19 mobility in Peru, d...

arXiv - Machine Learning · 4 min ·
[2506.17344] FFINO: Factorized Fourier Improved Neural Operator for Modeling Multiphase Flow in Underground Hydrogen Storage
Machine Learning

[2506.17344] FFINO: Factorized Fourier Improved Neural Operator for Modeling Multiphase Flow in Underground Hydrogen Storage

The paper presents FFINO, a novel neural operator for modeling multiphase flow in underground hydrogen storage, demonstrating significant...

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 ·
[2503.03178] Active operator learning with predictive uncertainty quantification for partial differential equations
Machine Learning

[2503.03178] Active operator learning with predictive uncertainty quantification for partial differential equations

The paper presents a lightweight predictive uncertainty quantification method for neural operators in solving partial differential equati...

arXiv - Machine Learning · 4 min ·
[2502.12981] Riemannian Variational Flow Matching for Material and Protein Design
Machine Learning

[2502.12981] Riemannian Variational Flow Matching for Material and Protein Design

The paper presents Riemannian Gaussian Variational Flow Matching (RG-VFM), a novel approach for generative modeling on curved manifolds, ...

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 ·
[2411.09847] Towards a Fairer Non-negative Matrix Factorization
Machine Learning

[2411.09847] Towards a Fairer Non-negative Matrix Factorization

This article presents a novel approach to Non-negative Matrix Factorization (NMF) aimed at improving fairness in machine learning algorit...

arXiv - Machine Learning · 4 min ·
[2410.18424] A Causal Graph-Enhanced Gaussian Process Regression for Modeling Engine-out NOx
Machine Learning

[2410.18424] A Causal Graph-Enhanced Gaussian Process Regression for Modeling Engine-out NOx

This paper presents a novel probabilistic model using Gaussian process regression to predict engine-out NOx emissions, enhancing predicti...

arXiv - Machine Learning · 4 min ·
[2602.22122] Probing the Geometry of Diffusion Models with the String Method
Machine Learning

[2602.22122] Probing the Geometry of Diffusion Models with the String Method

This article presents a novel framework using the string method to explore the geometry of diffusion models, enhancing understanding and ...

arXiv - Machine Learning · 4 min ·
[2602.22115] Slice and Explain: Logic-Based Explanations for Neural Networks through Domain Slicing
Machine Learning

[2602.22115] Slice and Explain: Logic-Based Explanations for Neural Networks through Domain Slicing

The paper presents a novel approach called 'Slice and Explain,' which utilizes domain slicing to enhance the efficiency of logic-based ex...

arXiv - Machine Learning · 3 min ·
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