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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 ·
Machine Learning

[D] ICML 2026 Average Score

Hi all, I’m curious about the current review dynamics for ICML 2026, especially after the rebuttal phase. For those who are reviewers (or...

Reddit - Machine Learning · 1 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 ·

All Content

[2602.20651] Sparse Bayesian Deep Functional Learning with Structured Region Selection
Machine Learning

[2602.20651] Sparse Bayesian Deep Functional Learning with Structured Region Selection

The paper presents a Sparse Bayesian Functional Deep Neural Network (sBayFDNN) that addresses the limitations of conventional functional ...

arXiv - Machine Learning · 3 min ·
[2602.20629] QEDBENCH: Quantifying the Alignment Gap in Automated Evaluation of University-Level Mathematical Proofs
Llms

[2602.20629] QEDBENCH: Quantifying the Alignment Gap in Automated Evaluation of University-Level Mathematical Proofs

The paper presents QEDBench, a benchmark for evaluating the alignment of automated systems in assessing university-level mathematical pro...

arXiv - Machine Learning · 4 min ·
[2602.20593] Is the Trigger Essential? A Feature-Based Triggerless Backdoor Attack in Vertical Federated Learning
Machine Learning

[2602.20593] Is the Trigger Essential? A Feature-Based Triggerless Backdoor Attack in Vertical Federated Learning

This paper presents a novel feature-based triggerless backdoor attack in vertical federated learning, demonstrating that triggers are not...

arXiv - Machine Learning · 4 min ·
[2602.20578] Upper-Linearizability of Online Non-Monotone DR-Submodular Maximization over Down-Closed Convex Sets
Machine Learning

[2602.20578] Upper-Linearizability of Online Non-Monotone DR-Submodular Maximization over Down-Closed Convex Sets

This paper explores online maximization of non-monotone Diminishing-Return submodular functions over down-closed convex sets, presenting ...

arXiv - Machine Learning · 3 min ·
[2602.20573] Benchmarking GNN Models on Molecular Regression Tasks with CKA-Based Representation Analysis
Machine Learning

[2602.20573] Benchmarking GNN Models on Molecular Regression Tasks with CKA-Based Representation Analysis

This paper benchmarks various Graph Neural Network (GNN) models for molecular regression tasks, highlighting the effectiveness of a hiera...

arXiv - Machine Learning · 4 min ·
[2602.20468] CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection
Ai Safety

[2602.20468] CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection

The CGSTA framework enhances multivariate time-series anomaly detection by utilizing dynamic layered graphs and stability-aware alignment...

arXiv - Machine Learning · 4 min ·
[2602.20463] A Long-Short Flow-Map Perspective for Drifting Models
Machine Learning

[2602.20463] A Long-Short Flow-Map Perspective for Drifting Models

This paper presents a novel perspective on drifting models through a long-short flow-map factorization, proposing a new likelihood learni...

arXiv - Machine Learning · 3 min ·
[2602.20403] Wasserstein Distributionally Robust Online Learning
Machine Learning

[2602.20403] Wasserstein Distributionally Robust Online Learning

This paper explores Wasserstein distributionally robust online learning, addressing challenges in convergence and computation through a n...

arXiv - Machine Learning · 3 min ·
[2602.20399] GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training
Machine Learning

[2602.20399] GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training

GeoPT introduces a novel approach to scaling physics simulations by utilizing lifted geometric pre-training, enhancing model efficiency a...

arXiv - Machine Learning · 3 min ·
[2602.20396] cc-Shapley: Measuring Multivariate Feature Importance Needs Causal Context
Machine Learning

[2602.20396] cc-Shapley: Measuring Multivariate Feature Importance Needs Causal Context

The paper introduces cc-Shapley, a method for measuring multivariate feature importance in machine learning by incorporating causal conte...

arXiv - Machine Learning · 3 min ·
[2602.20370] Quantitative Approximation Rates for Group Equivariant Learning
Machine Learning

[2602.20370] Quantitative Approximation Rates for Group Equivariant Learning

This paper explores quantitative approximation rates for group equivariant learning, demonstrating that equivariant architectures maintai...

arXiv - Machine Learning · 4 min ·
[2602.20329] CaDrift: A Time-dependent Causal Generator of Drifting Data Streams
Machine Learning

[2602.20329] CaDrift: A Time-dependent Causal Generator of Drifting Data Streams

The paper introduces CaDrift, a synthetic data generator that simulates time-dependent causal shifts in data streams, enhancing evaluatio...

arXiv - Machine Learning · 3 min ·
[2602.20307] In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks
Llms

[2602.20307] In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks

This paper presents In-Context Time-series Pre-training (ICTP), a framework that enhances time-series foundation models (TSFMs) with in-c...

arXiv - Machine Learning · 3 min ·
[2602.20296] Learning to Solve Complex Problems via Dataset Decomposition
Machine Learning

[2602.20296] Learning to Solve Complex Problems via Dataset Decomposition

This paper presents a novel approach to curriculum learning by decomposing complex datasets into simpler components, enhancing model trai...

arXiv - Machine Learning · 3 min ·
[2602.20293] Discrete Diffusion with Sample-Efficient Estimators for Conditionals
Machine Learning

[2602.20293] Discrete Diffusion with Sample-Efficient Estimators for Conditionals

This paper presents a novel discrete denoising diffusion framework that utilizes a sample-efficient estimator for single-site conditional...

arXiv - Machine Learning · 3 min ·
[2602.20232] Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions
Machine Learning

[2602.20232] Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions

The paper introduces MōLe, a machine learning architecture that predicts excitation amplitudes in coupled-cluster theory, enhancing molec...

arXiv - Machine Learning · 4 min ·
[2602.20194] FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessment
Machine Learning

[2602.20194] FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessment

This article presents a federated framework using a CTMC hazard model for assessing bridge deterioration, allowing municipalities to coll...

arXiv - Machine Learning · 4 min ·
[2602.02620] CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models
Machine Learning

[2602.02620] CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models

CryoLVM introduces a self-supervised learning model for cryo-electron microscopy (cryo-EM) density maps, enhancing structural representat...

arXiv - Machine Learning · 3 min ·
[2601.10611] Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding
Llms

[2601.10611] Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding

Molmo2 introduces a new family of open-weight vision-language models that excel in video understanding and grounding, featuring innovativ...

arXiv - AI · 4 min ·
[2601.09768] CLiMB: A Domain-Informed Novelty Detection Clustering Framework for Galactic Archaeology and Scientific Discovery
Machine Learning

[2601.09768] CLiMB: A Domain-Informed Novelty Detection Clustering Framework for Galactic Archaeology and Scientific Discovery

The paper presents CLiMB, a novel framework for novelty detection in galactic archaeology, enhancing clustering methods to identify unkno...

arXiv - AI · 4 min ·
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