E-swish: Adjusting Activations to Different Network Depths

22 January 2018

Papers citing "E-swish: Adjusting Activations to Different Network Depths"

7 / 7 papers shown

Title
Swish-T : Enhancing Swish Activation with Tanh Bias for Improved Neural Network Performance Youngmin Seo Jinha Kim Unsang Park 36 0 0 01 Jul 2024
Nonlinearity Enhanced Adaptive Activation Functions David Yevick 25 1 0 29 Mar 2024
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
A survey on modern trainable activation functions Andrea Apicella Francesco Donnarumma Francesco Isgrò R. Prevete 36 366 0 02 May 2020
Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learning Hock Hung Chieng Noorhaniza Wahid P. Ong Sai Raj Kishore Perla 22 41 0 15 Dec 2018
Adaptive Blending Units: Trainable Activation Functions for Deep Neural Networks L. R. Sütfeld Flemming Brieger Holger Finger S. Füllhase G. Pipa 28 28 0 26 Jun 2018