65 citations · 87 across the 5 of their papers we have counts for
6 papers
When Does Self-Supervision Help Graph Convolutional Networks?
Yuning You, Tianlong Chen, Zhangyang Wang +1
Self-supervision as an emerging technique has been employed to train convolutional neural networks (CNNs) for more transferrable, generalizable, and robust representation learning…
Network-principled deep generative models for designing drug combinations as graph sets
Mostafa Karimi, Arman Hasanzadeh, Yang shen
Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotic…
L-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
Yuning You, Tianlong Chen, Zhangyang Wang +1
Graph convolution networks (GCN) are increasingly popular in many applications, yet remain notoriously hard to train over large graph datasets. They need to compute node representa…
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts
Mostafa Karimi, Di Wu, Zhangyang Wang +1
Predicting compound-protein affinity is critical for accelerating drug discovery. Recent progress made by machine learning focuses on accuracy but leaves much to be desired for int…
Learning to Optimize in Swarms
Yue Cao, Tianlong Chen, Zhangyang Wang +1
Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks. Current such "meta-optimizers" often learn in the space of continuous…
Bayesian active learning for optimization and uncertainty quantification in protein docking
Yue Cao, Yang Shen
Motivation: Ab initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this…