155 citations · 183 across the 5 of their papers we have counts for
5 papers
Graph Contrastive Learning for Materials
Teddy Koker, Keegan Quigley, Will Spaeth +2
Recent work has shown the potential of graph neural networks to efficiently predict material properties, enabling high-throughput screening of materials. Training these models, how…
Learning to Prove Trigonometric Identities
Zhou Liu, Yujun Li, Zhengying Liu +2
Automatic theorem proving with deep learning methods has attracted attentions recently. In this paper, we construct an automatic proof system for trigonometric identities. We defin…
Scalable Geometric Deep Learning on Molecular Graphs
Nathan C. Frey, Siddharth Samsi, Joseph McDonald +3
Deep learning in molecular and materials sciences is limited by the lack of integration between applied science, artificial intelligence, and high-performance computing. Bottleneck…
Approximation Properties of Deep ReLU CNNs
Juncai He, Lin Li, Jinchao Xu
This paper focuses on establishing approximation properties for deep ReLU convolutional neural networks (CNNs) in two-dimensional space. The analysis is based on a decomposit…
ReLU Deep Neural Networks and Linear Finite Elements
Juncai He, Lin Li, Jinchao Xu +1
In this paper, we investigate the relationship between deep neural networks (DNN) with rectified linear unit (ReLU) function as the activation function and continuous piecewise lin…