The mathematics behind artificial intelligence (AI) and machine learning (ML) rely on linear algebra, calculus, probability, ...

Turing's 1950 paper didn't just pose the profound question, "Can machines think?". It ignited a quest to build AI technology ...

cGCN on the graph-represented data can be extended to fMRI data ... Lebo, et al. "Application of convolutional recurrent neural network for individual recognition based on resting state fmri data." ...

GCN, a groundbreaking disentangled graph convolutional network that dynamically adjusts feature channels for enhanced node ...

Many studies have used single-cell RNA sequencing (scRNA-seq) to infer gene regulatory networks (GRNs), which are crucial for ...

The interplay between graph analytics and large language models (LLMs) represents a promising frontier for advancing ...

Adolphi, C. and Sosonkina, M. (2025) Machine Learning and Simulation Techniques for Detecting Buoy Types from LiDAR Data.

To address this challenge, we proposed a concept-aware graph convolutional network (GCN) that utilizes cross-attentions to extract features unique to attributes and objects from paired concept-sharing ...

The authors develop an analysis package for characterizing the activity of neural dendrites and soma ... suggestive of network-level computational mechanisms.

Python package built to ease deep learning on graph, on top of existing DL frameworks.