39 citations · 71 across the 3 of their papers we have counts for
3 papers
cs.LG2019★ 23 cited
ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximations
Shanshan Tang, Bo Li, Haijun Yu
In a previous study [B. Li, S. Tang and H. Yu, Commun. Comput. Phy. 27(2):379-411, 2020], it is shown that deep neural networks built with rectified power units (RePU) as activatio…
cs.LG2019★ 9 cited
PowerNet: Efficient Representations of Polynomials and Smooth Functions by Deep Neural Networks with Rectified Power Units
Bo Li, Shanshan Tang, Haijun Yu
Deep neural network with rectified linear units (ReLU) is getting more and more popular recently. However, the derivatives of the function represented by a ReLU network are not con…
math.NA2019★ 39 cited
Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units
Bo Li, Shanshan Tang, Haijun Yu
Deep neural networks with rectified linear units (ReLU) are getting more and more popular due to their universal representation power and successful applications. Some theoretical…