TaLU: A Hybrid Activation Function Combining Tanh and Rectified Linear
Unit to Enhance Neural Networks

v1v2 (latest)

TaLU: A Hybrid Activation Function Combining Tanh and Rectified Linear Unit to Enhance Neural Networks

8 May 2023

Md. Ali Hossain

Azmain Yakin Srizon

ArXiv (abs)PDF HTML

Papers citing "TaLU: A Hybrid Activation Function Combining Tanh and Rectified Linear Unit to Enhance Neural Networks"

9 / 9 papers shown

Title
Review and Comparison of Commonly Used Activation Functions for Deep Neural Networks Tomasz Szandała 113 289 0 15 Oct 2020
Deep Learning using Rectified Linear Units (ReLU) Abien Fred Agarap 78 3,235 0 22 Mar 2018
Self-Normalizing Neural Networks Günter Klambauer Thomas Unterthiner Andreas Mayr Sepp Hochreiter 470 2,519 0 08 Jun 2017
Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning Stefan Elfwing E. Uchibe Kenji Doya 136 1,739 0 10 Feb 2017
Gaussian Error Linear Units (GELUs) Dan Hendrycks Kevin Gimpel 174 5,042 0 27 Jun 2016
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Djork-Arné Clevert Thomas Unterthiner Sepp Hochreiter 305 5,534 0 23 Nov 2015
Empirical Evaluation of Rectified Activations in Convolutional Network Bing Xu Naiyan Wang Tianqi Chen Mu Li 142 2,913 0 05 May 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 465 43,341 0 11 Feb 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He Xinming Zhang Shaoqing Ren Jian Sun VLM 338 18,651 0 06 Feb 2015