Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

Detecting which nodes in graphs are outliers is a relatively new machine learning task with
numerous applications. Despite the proliferation of algorithms developed in recent years for …

We present a scene representation that brings vision and touch into a shared 3D space
which we call a tactile-augmented radiance field. This representation capitalizes on two key …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks,
are receiving extensive attention for their powerful capability in learning node …

Large-scale graph training is a notoriously challenging problem for graph neural networks
(GNNs). Due to the nature of evolving graph structures into the training process, vanilla …

An emerging line of work has sought to generate plausible imagery from touch. Existing
approaches, however, tackle only narrow aspects of the visuo-tactile synthesis problem, and …

Graph Contrastive Learning (GCL) has emerged as a highly effective self-supervised
approach in graph representation learning. However, prevailing GCL methods confront two …

Recent works have demonstrated the benefits of capturing long-distance dependency in
graphs by deeper graph neural networks (GNNs). But deeper GNNs suffer from the long …