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...
Data analysis, statistics, and data engineering
UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...
Abstract page for arXiv paper 2601.21463: Unifying Speech Editing Detection and Content Localization via Prior-Enhanced Audio LLMs
Abstract page for arXiv paper 2601.02627: Improved Evidence Extraction and Metrics for Document Inconsistency Detection with LLMs
This paper explores the complexities of learning Boolean functions in the presence of two noise models: malicious and nasty noise, highli...
This article explores how Transformer models can learn sequences generated by Permuted Congruential Generators (PCGs), demonstrating thei...
This article evaluates self-supervised learning models for cardiac ultrasound view classification, comparing USF-MAE and MoCo v3 using th...
The paper presents TabImpute, a pre-trained transformer model designed for zero-shot imputation of missing data in tabular formats, signi...
The paper presents Flock, a knowledge graph foundation model that enhances zero-shot link prediction by employing probabilistic node-rela...
The paper introduces GenFacts, a generative framework for creating counterfactual explanations in multivariate time series, improving mod...
This article investigates the randomness in weight matrices of physics-informed neural networks (PINNs) and its impact on signal propagat...
Morephy-Net introduces an evolutionary multi-objective optimization method for physics-informed neural operator learning networks, enhanc...
This article presents a novel approach to out-of-distribution detection in arc welding quality prediction, enhancing continual learning b...
This paper presents an explainable AI framework for analyzing cough sounds linked to chronic respiratory diseases, focusing on COPD. It u...
This article evaluates uncertainty estimates in binary classification models, comparing six probabilistic machine learning algorithms to ...
This paper presents novel algorithms for selecting the arm with the highest variance among independent arms, focusing on misallocation mi...
The paper introduces Qronos, a novel post-training quantization algorithm that enhances neural network performance by correcting quantiza...
This article presents a Hybrid Quantum Recurrent Neural Network framework for predicting the remaining useful life of jet engines, showca...
This article explores how global calibration enhances multiaccuracy in machine learning, revealing its potential to improve predictive fa...
This paper presents a novel method for off-policy learning that addresses unobserved confounding, enhancing the accuracy of policy learni...
This article presents a comprehensive study on training long-context visual document models, achieving state-of-the-art performance in vi...
This study analyzes AI research production across European regions at the NUTS-3 level, highlighting the specialization of peripheral reg...
This article presents a theoretical framework for clone-robust weighting functions in metric spaces, addressing redundancy bias in benchm...
The CAAT-EHR paper presents a novel Cross-Attentional Autoregressive Transformer model designed to generate generalizable embeddings from...
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