Deep Learning via Dynamical Systems: An Approximation Perspective

Deep Learning via Dynamical Systems: An Approximation Perspective

22 December 2019

Papers citing "Deep Learning via Dynamical Systems: An Approximation Perspective"

12 / 62 papers shown

Title
Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers Samuel C. Chevalier Jochen Stiasny Spyros Chatzivasileiadis 27 3 0 04 Jun 2021
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth Zuowei Shen Haizhao Yang Shijun Zhang 103 115 0 28 Feb 2021
Sparsity in long-time control of neural ODEs C. Yagüe Borjan Geshkovski 10 8 0 26 Feb 2021
Momentum Residual Neural Networks Michael E. Sander Pierre Ablin Mathieu Blondel Gabriel Peyré 27 57 0 15 Feb 2021
Universal Approximation Property of Neural Ordinary Differential Equations Takeshi Teshima Koichi Tojo Masahiro Ikeda Isao Ishikawa Kenta Oono 22 40 0 04 Dec 2020
Neural Network Approximation: Three Hidden Layers Are Enough Zuowei Shen Haizhao Yang Shijun Zhang 30 115 0 25 Oct 2020
ResNet After All? Neural ODEs and Their Numerical Solution Katharina Ott P. Katiyar Philipp Hennig Michael Tiemann 33 29 0 30 Jul 2020
Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory Paulo Tabuada Bahman Gharesifard 19 25 0 12 Jul 2020
Deep Network with Approximation Error Being Reciprocal of Width to Power of Square Root of Depth Zuowei Shen Haizhao Yang Shijun Zhang 6 7 0 22 Jun 2020
Dissecting Neural ODEs Stefano Massaroli Michael Poli Jinkyoo Park Atsushi Yamashita Hajime Asama 10 198 0 19 Feb 2020
Deep Network Approximation for Smooth Functions Jianfeng Lu Zuowei Shen Haizhao Yang Shijun Zhang 67 247 0 09 Jan 2020
Approximation Capabilities of Neural ODEs and Invertible Residual Networks Han Zhang Xi Gao Jacob Unterman Tom Arodz 19 98 0 30 Jul 2019