[Neurips2022] Understanding Non-linearity in Graph Neural Networks from the Perspective of Bayesian Inference - Graph-COM/Bayesian_inference_based_GNN Skip to content Navigation Menu ...

Non-linear data processing is a great way to get there, ... mesh or graph). The greater challenges arise as data and compute patterns become increasingly complex and non-linear, ...

Graph algorithms consist of a non-linear data structure of nodes (vertices) and edges (relationships between nodes). These programming algorithms are essential for graph manipulation, making them ...

Estimating the unknown causal dependencies among graph-connected time series plays an important role in many applications, such as sensor network analysis, signal processing over cyber-physical ...