A new spatial count data model with Bayesian additive regression trees for accident hot spot identification

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The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

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A new spatial count data model with Bayesian additive regression trees for accident hot spot identification

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Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT

ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Perspective on nonvolatile magnon-signal storage and in-memory computation for low-power consuming magnonics

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Diffusion Stochastic Optimization for Min-Max Problems

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Topics in statistical physics of high-dimensional machine learning

Machine Learning in Decision-Making Systems: Fairness, Robustness, and Data Bias

Driving and suppressing the human language network using large language models

Enhancing skin lesion classification: a CNN approach with human baseline comparison

Robust machine learning for neuroscientific inference

Neural Garment Modeling

Understanding generalization and robustness in modern deep learning

Adapting Fine-Grained Cross-View Localization to Areas Without Fine Ground Truth

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet