60 citations · 202 across the 22 of their papers we have counts for
3 papers · 1 filter
High-Order Approximation Rates for Shallow Neural Networks with Cosine and ReLU Activation Functions
Jonathan W. Siegel, Jinchao Xu
We study the approximation properties of shallow neural networks with an activation function which is a power of the rectified linear unit. Specifically, we consider the dependence…
Extensible Structure-Informed Prediction of Formation Energy with Improved Accuracy and Usability employing Neural Networks
Adam M. Krajewski, Jonathan W. Siegel, Jinchao Xu +1
In the present paper, we introduce a new neural network-based tool for the prediction of formation energies of atomic structures based on elemental and structural features of Voron…
Training Sparse Neural Networks using Compressed Sensing
Jonathan W. Siegel, Jianhong Chen, Pengchuan Zhang +1
Pruning the weights of neural networks is an effective and widely-used technique for reducing model size and inference complexity. We develop and test a novel method based on compr…