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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

[2602.20198] KEMP-PIP: A Feature-Fusion Based Approach for Pro-inflammatory Peptide Prediction
Llms

[2602.20198] KEMP-PIP: A Feature-Fusion Based Approach for Pro-inflammatory Peptide Prediction

The article presents KEMP-PIP, a novel hybrid machine learning framework designed for predicting pro-inflammatory peptides by integrating...

arXiv - Machine Learning · 3 min ·
[2602.20178] Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis
Machine Learning

[2602.20178] Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis

This paper presents a data-driven approach to Multiuser Multiple-Input Multiple-Output (MU-MIMO) detection, introducing a novel architect...

arXiv - Machine Learning · 4 min ·
[2602.20195] OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs
Machine Learning

[2602.20195] OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs

The paper presents OrgFlow, a generative model designed to predict organic crystal structures from molecular graphs, addressing a signifi...

arXiv - Machine Learning · 3 min ·
[2602.18431] SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary
Machine Learning

[2602.18431] SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary

The paper presents SMaRT, an innovative algorithm for online resource allocation in the Kenyan judiciary, focusing on mediator assignment...

arXiv - Machine Learning · 4 min ·
[2602.21191] Statistical Query Lower Bounds for Smoothed Agnostic Learning
Machine Learning

[2602.21191] Statistical Query Lower Bounds for Smoothed Agnostic Learning

This paper presents a Statistical Query lower bound for smoothed agnostic learning, focusing on the complexity of learning halfspaces und...

arXiv - Machine Learning · 4 min ·
[2602.21185] The Diffusion Duality, Chapter II: $Ψ$-Samplers and Efficient Curriculum
Machine Learning

[2602.21185] The Diffusion Duality, Chapter II: $Ψ$-Samplers and Efficient Curriculum

This article presents advancements in discrete diffusion models, introducing Predictor-Corrector samplers that enhance sampling efficienc...

arXiv - Machine Learning · 3 min ·
[2602.21168] Sequential Counterfactual Inference for Temporal Clinical Data: Addressing the Time Traveler Dilemma
Machine Learning

[2602.21168] Sequential Counterfactual Inference for Temporal Clinical Data: Addressing the Time Traveler Dilemma

This article presents a novel Sequential Counterfactual Framework for analyzing temporal clinical data, addressing limitations of traditi...

arXiv - Machine Learning · 4 min ·
[2602.21104] Ski Rental with Distributional Predictions of Unknown Quality
Machine Learning

[2602.21104] Ski Rental with Distributional Predictions of Unknown Quality

This paper explores the ski rental problem within the framework of distributional predictions, presenting an algorithm that minimizes exp...

arXiv - Machine Learning · 4 min ·
[2602.21078] ProxyFL: A Proxy-Guided Framework for Federated Semi-Supervised Learning
Machine Learning

[2602.21078] ProxyFL: A Proxy-Guided Framework for Federated Semi-Supervised Learning

The article presents ProxyFL, a novel framework for Federated Semi-Supervised Learning (FSSL) that addresses data heterogeneity issues by...

arXiv - Machine Learning · 4 min ·
[2602.21046] PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis
Machine Learning

[2602.21046] PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis

The paper presents PIME, a novel framework for interpretable brain network analysis using Monte Carlo Tree Search (MCTS) to enhance disor...

arXiv - Machine Learning · 3 min ·
[2602.21043] T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation
Ai Startups

[2602.21043] T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation

The paper presents T1, a CNN-Transformer hybrid model for robust multivariate time-series imputation, achieving state-of-the-art performa...

arXiv - Machine Learning · 4 min ·
[2602.20974] MAST: A Multi-fidelity Augmented Surrogate model via Spatial Trust-weighting
Machine Learning

[2602.20974] MAST: A Multi-fidelity Augmented Surrogate model via Spatial Trust-weighting

The paper introduces MAST, a Multi-fidelity Augmented Surrogate model that improves predictive accuracy in engineering design by effectiv...

arXiv - Machine Learning · 3 min ·
[2602.20947] Estimation of Confidence Bounds in Binary Classification using Wilson Score Kernel Density Estimation
Machine Learning

[2602.20947] Estimation of Confidence Bounds in Binary Classification using Wilson Score Kernel Density Estimation

This article presents a novel method for estimating confidence bounds in binary classification using Wilson Score Kernel Density Estimati...

arXiv - Machine Learning · 4 min ·
[2602.20937] Extending $μ$P: Spectral Conditions for Feature Learning Across Optimizers
Llms

[2602.20937] Extending $μ$P: Spectral Conditions for Feature Learning Across Optimizers

This article presents a framework for extending the maximal update parameterization ($μ$P) to various optimizers, enhancing feature learn...

arXiv - Machine Learning · 4 min ·
[2602.20921] On the Generalization Behavior of Deep Residual Networks From a Dynamical System Perspective
Machine Learning

[2602.20921] On the Generalization Behavior of Deep Residual Networks From a Dynamical System Perspective

This paper explores the generalization behavior of deep residual networks (ResNets) through a dynamical systems framework, establishing n...

arXiv - Machine Learning · 3 min ·
[2602.20758] Deep unfolding of MCMC kernels: scalable, modular & explainable GANs for high-dimensional posterior sampling
Machine Learning

[2602.20758] Deep unfolding of MCMC kernels: scalable, modular & explainable GANs for high-dimensional posterior sampling

This article presents a novel approach to integrating deep unfolding techniques with MCMC methods, enhancing the efficiency and interpret...

arXiv - Machine Learning · 4 min ·
[2602.20782] On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning
Machine Learning

[2602.20782] On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning

This paper explores electric vehicle energy demand forecasting using federated learning, comparing various forecasting methodologies to e...

arXiv - Machine Learning · 4 min ·
[2602.20714] WeirNet: A Large-Scale 3D CFD Benchmark for Geometric Surrogate Modeling of Piano Key Weirs
Machine Learning

[2602.20714] WeirNet: A Large-Scale 3D CFD Benchmark for Geometric Surrogate Modeling of Piano Key Weirs

WeirNet introduces a comprehensive 3D CFD benchmark dataset for modeling the hydraulic performance of Piano Key Weirs, facilitating faste...

arXiv - Machine Learning · 4 min ·
[2602.20698] High-Dimensional Robust Mean Estimation with Untrusted Batches
Machine Learning

[2602.20698] High-Dimensional Robust Mean Estimation with Untrusted Batches

This paper presents algorithms for high-dimensional mean estimation in collaborative settings where data may come from untrusted sources,...

arXiv - Machine Learning · 4 min ·
[2602.20671] Bikelution: Federated Gradient-Boosting for Scalable Shared Micro-Mobility Demand Forecasting
Machine Learning

[2602.20671] Bikelution: Federated Gradient-Boosting for Scalable Shared Micro-Mobility Demand Forecasting

The paper presents Bikelution, a federated learning approach for predicting demand in shared micro-mobility systems, addressing privacy c...

arXiv - Machine Learning · 3 min ·
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