graph neural networks

The Drift Neural Network: A Neuro-Symbolic Cognitive Architecture for Autonomous Systems

The Drift Neural Network is a neuro-symbolic cognitive architecture that joins four faculties in one closed loop, offered against the prevailing bet on scaling the autoregressive transformer. A transformer produces output by statistical continuation over a fixed matrix of weights, and four of its limits are structural,...

Structured PREreview of "Training, learning and inference: unified dynamics of neural systems"

This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22899514. Does the introduction explain the objective of the research presented in the preprint? Yes The introduction clearly establishes the motivation, core research ob...

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

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

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

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...

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...

Hypergraph Neural Networks Approach for 3-Dimensional Matching Problem

Dynamic time-lagged relation graph structure learning for multivariate time series forecasting

TGCAE: temporal graph convolutional autoencoder for robust anomaly detection in object-centric process mining

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...