graph neural networks

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (video)

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (video)

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (video)

Toward interpretable multiclass semantic intelligence: explainable brain tumor segmentation and automated grade stratification

Abstract Purpose Accurate brain tumor grade classification from magnetic resonance imaging is essential for diagnosis, treatment planning, and prognosis assessment. Although deep learning models have demonstrated strong performance, many lack clinical validation, robustness across datasets, and interpretability for dec...

PiMorph: proposal checkpoints, benchmark results and training tiles for endothelial cell complexes

Proposal checkpoints, benchmark results and training tiles for PiMorph, which reconstructs endothelial monolayers from fluorescence microscopy as embedded cell complexes: cells, gaps, cell-cell contacts and multicellular vertices with exact incidence, plus a posterior over legal complexes for every field. The code, the...

p–LSGR: A lightweight post–hoc latent–space resampling framework for imbalanced and complex graph distributions

Graph neural networks (GNNs) are widely used across domains but remain sensitive to class imbalance, class overlap, and complex data distributions, limiting reliability in real-world settings. Existing imbalance-mitigation strategies provide only partial robustness, are often computationally expensive, and leave post-h...

Graph Neural Network Enhanced Stochastic Optimization for Reliability Assessment of Composite Power Systems

The increasing penetration of wind power intensifies the uncertainty and variability of power system operation. Monte Carlo simulation (MCS)-based reliability assessment of wind-integrated composite power systems usually requires repeated optimization over numerous operating states, thereby imposing a considerable comp...

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (slides) - Slides

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (slides) - Slides

CiteJustice: Automated Legal Judgment Prediction for Indian Courts Using Graph Neural Networks

CiteJustice is an automated legal judgment prediction framework designed for Indian courts. Unlike conventional approaches that rely primarily on semantic similarity between a new case and past judgments, CiteJustice incorporates the evolving authority of legal precedents through a Dynamic Precedent Evolution Graph (DP...

A MACHINE LEARNING SURROGATE FOR ALPINE3D SNOWPACK SIMULATION: PERSISTENCE-BASED EVALUATION AND A PATH TOWARD SNOWDRIFT

Distributed snow-cover models such as Alpine3D and SNOWPACK provide spatially detailed information on snowpack evolution, surface energy balance, and terrain-driven variability that is relevant to avalanche operations.The drawback is the computational cost of high-resolution, multiseason simulations, which limits repea...

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model (video)