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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 ·

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[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 ·
[2602.21701] Learning Complex Physical Regimes via Coverage-oriented Uncertainty Quantification: An application to the Critical Heat Flux
Machine Learning

[2602.21701] Learning Complex Physical Regimes via Coverage-oriented Uncertainty Quantification: An application to the Critical Heat Flux

This article explores the application of coverage-oriented uncertainty quantification (UQ) in scientific machine learning, focusing on th...

arXiv - Machine Learning · 4 min ·
[2602.21693] TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts
Machine Learning

[2602.21693] TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts

The paper introduces TiMi, a novel approach that enhances time series forecasting by integrating multimodal data through a Mixture of Exp...

arXiv - Machine Learning · 4 min ·
[2602.21648] Multimodal Survival Modeling and Fairness-Aware Clinical Machine Learning for 5-Year Breast Cancer Risk Prediction
Machine Learning

[2602.21648] Multimodal Survival Modeling and Fairness-Aware Clinical Machine Learning for 5-Year Breast Cancer Risk Prediction

This article presents a multimodal machine learning framework for predicting 5-year breast cancer survival, integrating clinical and geno...

arXiv - Machine Learning · 4 min ·
[2602.21634] AgentLTV: An Agent-Based Unified Search-and-Evolution Framework for Automated Lifetime Value Prediction
Machine Learning

[2602.21634] AgentLTV: An Agent-Based Unified Search-and-Evolution Framework for Automated Lifetime Value Prediction

AgentLTV introduces an agent-based framework for automated Lifetime Value (LTV) prediction, enhancing model discovery and performance in ...

arXiv - Machine Learning · 4 min ·
[2602.21597] NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Training
Machine Learning

[2602.21597] NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Training

The paper presents NGDB-Zoo, a framework designed to enhance the training efficiency of Neural Graph Databases (NGDBs) by decoupling logi...

arXiv - Machine Learning · 3 min ·
[2602.21601] Deep Clustering based Boundary-Decoder Net for Inter and Intra Layer Stress Prediction of Heterogeneous Integrated IC Chip
Machine Learning

[2602.21601] Deep Clustering based Boundary-Decoder Net for Inter and Intra Layer Stress Prediction of Heterogeneous Integrated IC Chip

This article presents a novel approach using a Deep Clustering based Boundary-Decoder Net for predicting inter and intra-layer stress in ...

arXiv - Machine Learning · 4 min ·
[2602.21588] ABM-UDE: Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning
Machine Learning

[2602.21588] ABM-UDE: Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning

The paper presents ABM-UDE, a method for creating efficient surrogates for epidemic agent-based models using scientific machine learning,...

arXiv - Machine Learning · 4 min ·
[2602.21550] Extending Sequence Length is Not All You Need: Effective Integration of Multimodal Signals for Gene Expression Prediction
Machine Learning

[2602.21550] Extending Sequence Length is Not All You Need: Effective Integration of Multimodal Signals for Gene Expression Prediction

This article presents a novel approach to gene expression prediction by integrating multimodal epigenomic signals, challenging the relian...

arXiv - Machine Learning · 4 min ·
[2602.21508] WaterVIB: Learning Minimal Sufficient Watermark Representations via Variational Information Bottleneck
Machine Learning

[2602.21508] WaterVIB: Learning Minimal Sufficient Watermark Representations via Variational Information Bottleneck

The paper introduces WaterVIB, a framework for robust watermarking that utilizes the Variational Information Bottleneck to enhance resili...

arXiv - Machine Learning · 3 min ·
[2602.21472] The Design Space of Tri-Modal Masked Diffusion Models
Llms

[2602.21472] The Design Space of Tri-Modal Masked Diffusion Models

This paper introduces the first tri-modal masked diffusion model, pretrained on text, image-text, and audio-text data, analyzing its perf...

arXiv - Machine Learning · 4 min ·
[2602.21498] Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting
Ai Startups

[2602.21498] Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting

The paper presents ReIMTS, a new approach for forecasting irregular multivariate time series by preserving original timestamps and captur...

arXiv - Machine Learning · 3 min ·
[2602.21469] D-Flow SGLD: Source-Space Posterior Sampling for Scientific Inverse Problems with Flow Matching
Machine Learning

[2602.21469] D-Flow SGLD: Source-Space Posterior Sampling for Scientific Inverse Problems with Flow Matching

The paper presents D-Flow SGLD, a method for source-space posterior sampling in scientific inverse problems, enhancing fidelity and uncer...

arXiv - Machine Learning · 4 min ·
[2602.21466] Asymptotically Fast Clebsch-Gordan Tensor Products with Vector Spherical Harmonics
Machine Learning

[2602.21466] Asymptotically Fast Clebsch-Gordan Tensor Products with Vector Spherical Harmonics

This article presents a novel algorithm for computing Clebsch-Gordan tensor products using vector spherical harmonics, achieving signific...

arXiv - Machine Learning · 4 min ·
[2602.21462] Effects of Training Data Quality on Classifier Performance
Machine Learning

[2602.21462] Effects of Training Data Quality on Classifier Performance

This paper investigates how the quality of training data affects the performance of various classifiers, particularly in metagenomic asse...

arXiv - Machine Learning · 3 min ·
[2602.21426] Proximal-IMH: Proximal Posterior Proposals for Independent Metropolis-Hastings with Approximate Operators
Machine Learning

[2602.21426] Proximal-IMH: Proximal Posterior Proposals for Independent Metropolis-Hastings with Approximate Operators

The paper introduces Proximal-IMH, a novel sampling method for Bayesian inverse problems that enhances the efficiency of the Independent ...

arXiv - Machine Learning · 3 min ·
[2602.21408] Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates
Ai Infrastructure

[2602.21408] Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates

This article presents Generative Bayesian Computation (GBC) as a scalable alternative to Gaussian Process (GP) surrogates, addressing lim...

arXiv - Machine Learning · 4 min ·
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