PowerNet: Efficient Representations of Polynomials and Smooth Functions by Deep Neural Networks with Rectified Power Units

9 September 2019

Papers citing "PowerNet: Efficient Representations of Polynomials and Smooth Functions by Deep Neural Networks with Rectified Power Units"

7 / 7 papers shown

Title
Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation Madison Cooley Shandian Zhe Robert M. Kirby Varun Shankar 199 1 0 04 Jun 2024
Differentiable Neural Networks with RePU Activation: with Applications to Score Estimation and Isotonic Regression Guohao Shen Yuling Jiao Yuanyuan Lin Jian Huang 123 3 0 01 May 2023
Theoretical guarantees for neural control variates in MCMC Denis Belomestny Artur Goldman A. Naumov S. Samsonov BDL DRL 48 6 0 03 Apr 2023
Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks Guohao Shen Yuling Jiao Yuanyuan Lin J. Horowitz Jian Huang 44 4 0 21 Jul 2022
Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations Denis Belomestny A. Naumov Nikita Puchkin S. Samsonov 42 21 0 20 Jun 2022
De Rham compatible Deep Neural Network FEM M. Longo J. Opschoor Nico Disch Christoph Schwab Jakob Zech 85 8 0 14 Jan 2022
Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark S. Dubey S. Singh B. B. Chaudhuri 124 690 0 29 Sep 2021