Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks

16 October 2018

Papers citing "Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks"

26 / 26 papers shown

Title
Evolutionary Architecture Search For Deep Multitask Networks J. Liang Elliot Meyerson Risto Miikkulainen 54 120 0 10 Mar 2018
Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari P. Chrabaszcz I. Loshchilov Frank Hutter 39 100 0 24 Feb 2018
Regularized Evolution for Image Classifier Architecture Search Esteban Real A. Aggarwal Yanping Huang Quoc V. Le 115 3,009 0 05 Feb 2018
Improving Generalization Performance by Switching from Adam to SGD N. Keskar R. Socher ODL 57 522 0 20 Dec 2017
ES Is More Than Just a Traditional Finite-Difference Approximator Joel Lehman Jay Chen Jeff Clune Kenneth O. Stanley 42 89 0 18 Dec 2017
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning F. Such Vashisht Madhavan Edoardo Conti Joel Lehman Kenneth O. Stanley Jeff Clune 56 688 0 18 Dec 2017
On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent Xingwen Zhang Jeff Clune Kenneth O. Stanley 39 57 0 18 Dec 2017
Progressive Neural Architecture Search Chenxi Liu Barret Zoph Maxim Neumann Jonathon Shlens Wei Hua Li Li Li Fei-Fei Alan Yuille Jonathan Huang Kevin Patrick Murphy 54 1,986 0 02 Dec 2017
Population Based Training of Neural Networks Max Jaderberg Valentin Dalibard Simon Osindero Wojciech M. Czarnecki Jeff Donahue ... Tim Green Iain Dunning Karen Simonyan Chrisantha Fernando Koray Kavukcuoglu 32 736 0 27 Nov 2017
Fraternal Dropout Konrad Zolna Devansh Arpit Dendi Suhubdy Yoshua Bengio 33 53 0 31 Oct 2017
Gradient-free Policy Architecture Search and Adaptation Sayna Ebrahimi Anna Rohrbach Trevor Darrell 36 29 0 16 Oct 2017
Regularizing and Optimizing LSTM Language Models Stephen Merity N. Keskar R. Socher 126 1,093 0 07 Aug 2017
The Marginal Value of Adaptive Gradient Methods in Machine Learning Ashia Wilson Rebecca Roelofs Mitchell Stern Nathan Srebro Benjamin Recht ODL 41 1,023 0 23 May 2017
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures Masanori Suganuma Shinichi Shirakawa T. Nagao 76 589 0 03 Apr 2017
Evolution Strategies as a Scalable Alternative to Reinforcement Learning Tim Salimans Jonathan Ho Xi Chen Szymon Sidor Ilya Sutskever 54 1,523 0 10 Mar 2017
Large-Scale Evolution of Image Classifiers Esteban Real Sherry Moore Andrew Selle Saurabh Saxena Y. Suematsu Jie Tan Quoc V. Le Alexey Kurakin 79 1,631 0 03 Mar 2017
Evolving Deep Neural Networks Risto Miikkulainen J. Liang Elliot Meyerson Aditya Rawal Daniel Fink ... B. Raju Hormoz Shahrzad Arshak Navruzyan Nigel P. Duffy Babak Hodjat 58 886 0 01 Mar 2017
Temporal Ensembling for Semi-Supervised Learning S. Laine Timo Aila UQCV 147 2,543 0 07 Oct 2016
Dropout with Expectation-linear Regularization Xuezhe Ma Yingkai Gao Zhiting Hu Yaoliang Yu Yuntian Deng Eduard H. Hovy UQCV 30 50 0 26 Sep 2016
SGDR: Stochastic Gradient Descent with Warm Restarts I. Loshchilov Frank Hutter ODL 192 8,030 0 13 Aug 2016
Optimization Methods for Large-Scale Machine Learning Léon Bottou Frank E. Curtis J. Nocedal 165 3,191 0 15 Jun 2016
The CMA Evolution Strategy: A Tutorial N. Hansen 41 1,362 0 04 Apr 2016
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks Y. Gal Zoubin Ghahramani UQCV DRL BDL 93 1,644 0 16 Dec 2015
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 998 192,638 0 10 Dec 2015
LM-CMA: an Alternative to L-BFGS for Large Scale Black-box Optimization I. Loshchilov ODL 21 60 0 01 Nov 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 421 149,474 0 22 Dec 2014