Learning Activation Functions: A new paradigm for understanding Neural Networks

23 June 2019

Papers citing "Learning Activation Functions: A new paradigm for understanding Neural Networks"

10 / 10 papers shown

Title
Learnable polynomial, trigonometric, and tropical activations Ismail Khalfaoui-Hassani Stefan Kesselheim 64 0 0 03 Feb 2025
KAN: Kolmogorov-Arnold Networks Ziming Liu Yixuan Wang Sachin Vaidya Fabian Ruehle James Halverson Marin Soljacic Thomas Y. Hou Max Tegmark 98 485 0 30 Apr 2024
Learning Specialized Activation Functions for Physics-informed Neural Networks Honghui Wang Lu Lu Shiji Song Gao Huang PINN AI4CE 16 12 0 08 Aug 2023
Efficient Activation Function Optimization through Surrogate Modeling G. Bingham Risto Miikkulainen 24 2 0 13 Jan 2023
How important are activation functions in regression and classification? A survey, performance comparison, and future directions Ameya Dilip Jagtap George Karniadakis AI4CE 37 71 0 06 Sep 2022
Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark S. Dubey S. Singh B. B. Chaudhuri 43 643 0 29 Sep 2021
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions Ameya Dilip Jagtap Yeonjong Shin Kenji Kawaguchi George Karniadakis ODL 45 131 0 20 May 2021
The Representation Theory of Neural Networks M. Armenta Pierre-Marc Jodoin 29 30 0 23 Jul 2020
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks Alejandro Molina P. Schramowski Kristian Kersting ODL 23 79 0 15 Jul 2019
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 186 1,186 0 30 Nov 2014