52 citations · 58 across the 4 of their papers we have counts for
6 papers
Equivariant Graph Neural Networks for 3D Macromolecular Structure
Bowen Jing, Stephan Eismann, Pratham N. Soni +1
Representing and reasoning about 3D structures of macromolecules is emerging as a distinct challenge in machine learning. Here, we extend recent work on geometric vector perceptron…
Protein model quality assessment using rotation-equivariant, hierarchical neural networks
Stephan Eismann, Patricia Suriana, Bowen Jing +2
Proteins are miniature machines whose function depends on their three-dimensional (3D) structure. Determining this structure computationally remains an unsolved grand challenge. A…
Rotation-Invariant Gait Identification with Quaternion Convolutional Neural Networks
Bowen Jing, Vinay Prabhu, Angela Gu +1
A desireable property of accelerometric gait-based identification systems is robustness to new device orientations presented by users during testing but unseen during the training…
Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes
Stephan Eismann, Raphael J. L. Townshend, Nathaniel Thomas +3
Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure pred…
SGVAE: Sequential Graph Variational Autoencoder
Bowen Jing, Ethan A. Chi, Jillian Tang
Generative models of graphs are well-known, but many existing models are limited in scalability and expressivity. We present a novel sequential graphical variational autoencoder op…
Modeling Sensorimotor Coordination as Multi-Agent Reinforcement Learning with Differentiable Communication
Bowen Jing, William Yin
Multi-agent reinforcement learning has shown promise on a variety of cooperative tasks as a consequence of recent developments in differentiable inter-agent communication. However,…