79 citations · 110 across the 7 of their papers we have counts for
7 papers
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
Zhizhou Ren, Anji Liu, Yitao Liang +2
Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning t…
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
Yinan Huang, Xingang Peng, Jianzhu Ma +1
Deep learning has achieved tremendous success in designing novel chemical compounds with desirable pharmaceutical properties. In this work, we focus on a new type of drug design pr…
Equivariant Point Cloud Analysis via Learning Orientations for Message Passing
Shitong Luo, Jiahan Li, Jiaqi Guan +4
Equivariance has been a long-standing concern in various fields ranging from computer vision to physical modeling. Most previous methods struggle with generality, simplicity, and e…
Provable Constrained Stochastic Convex Optimization with XOR-Projected Gradient Descent
Fan Ding, Yijie Wang, Jianzhu Ma +1
Provably solving stochastic convex optimization problems with constraints is essential for various problems in science, business, and statistics. Recently proposed XOR-Stochastic G…
Device-system Co-design of Photonic Neuromorphic Processor using Reinforcement Learning
Yingheng Tang, Princess Tara Zamani, Ruiyang Chen +4
The incorporation of high-performance optoelectronic devices into photonic neuromorphic processors can substantially accelerate computationally intensive operations in machine lear…
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
Susheel Suresh, Vinith Budde, Jennifer Neville +2
Graph neural networks (GNNs) have achieved tremendous success on multiple graph-based learning tasks by fusing network structure and node features. Modern GNN models are built upon…