Graph Convolutional Networks (GCNs) are a class of general models that can learn from graph structured data.
We demonstrate the effectiveness of our estimators on synthetic benchmarks and a real world fMRI data, with application of inter-subject correlation analysis.
We aim to better understand attention over nodes in graph neural networks (GNNs) and identify factors influencing its effectiveness.
Ranked #20 on Graph Classification on PROTEINS
Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data.
Ranked #8 on Graph Classification on NCI109
We demonstrate an intelligent conversational agent system designed for advancing human-machine collaborative tasks.
A popular testbed for deep learning has been multimodal recognition of human activity or gesture involving diverse inputs such as video, audio, skeletal pose and depth images.
We propose a new model that enhances the CRBM model with a factored multi-task component to become Multi-Task Conditional Restricted Boltzmann Machines (MTCRBMs).
We present a novel approach to computational modeling of social interactions based on modeling of essential social interaction predicates (ESIPs) such as joint attention and entrainment.