Fetching the paper…
Reading the bibliography…
The design of neural network architectures for a new data set is a laborious task which requires human deep learning expertise.
The Algorithm Selection Problem
John R. Rice · 1976
Earlier work this paper cites.
Designing Neural Networks using Genetic Algorithms
Geoffrey F. Miller, Peter M. Todd, and Shailesh U. Hegde · 1989
Earlier work this paper cites.
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Ronald J. Williams · 1992
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann Lecun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Finite-time Analysis of the Multiarmed Bandit Problem
Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer · 2002
Earlier work this paper cites.
Evolving Neural Networks Through Augmenting Topologies
Kenneth O. Stanley and Risto Miikkulainen · 2002
Earlier work this paper cites.
Modification of UCT with patterns in Monte-Carlo Go
Sylvain Gelly, Yizao Wang, Rémi Munos, and Olivier Teytaud · 2006
Earlier work this paper cites.
Bandit Based Monte-Carlo Planning
Levente Kocsis and Csaba Szepesvári · 2006
Earlier work this paper cites.
Combining online and offline knowledge in UCT
Sylvain Gelly and David Silver · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Monte-Carlo Planning in Large POMDPs
David Silver and Joel Veness · 2010
Earlier work this paper cites.
Reading Digits in Natural Images with Unsupervised Feature Learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
Cited alongside, same era.
Random Search for Hyper-Parameter Optimization
James Bergstra and Yoshua Bengio · 2012
Cited alongside, same era.
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
George E. Dahl, Dong Yu, Li Deng, and Alex Acero · 2012
Cited alongside, same era.
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P. Adams · 2012
Cited alongside, same era.
Maxout Networks
Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron C. Courville, and Yoshua Bengio · 2013
Cited alongside, same era.
Min Lin, Qiang Chen, and Shuicheng Yan · 2013
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, and Ryan P. Adams · 2015
Later among the works it cites.
Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
Later among the works it cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Later among the works it cites.
Net2Net: Accelerating Learning via Knowledge Transfer
Tianqi Chen, Ian J. Goodfellow, and Jonathon Shlens · 2016
Later among the works it cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Later among the works it cites.
Towards Automatically-Tuned Neural Networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2014
Cited alongside, same era.
FitNets: Hints for Thin Deep Nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
Cited alongside, same era.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller · 2014
Cited alongside, same era.
Sequence to Sequence Learning with Neural Networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
Cited alongside, same era.
Freeze-Thaw Bayesian Optimization
Kevin Swersky, Jasper Snoek, and Ryan Prescott Adams · 2014
Cited alongside, same era.
Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, and Frank Hutter · 2016
Later among the works it cites.
A Brief Survey of Deep Reinforcement Learning
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath · 2017
Closest in time.
Designing Neural Network Architectures using Reinforcement Learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2017
Closest in time.
Reinforcement Learning for Architecture Search by Network Transformation
Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang · 2017
Closest in time.
DeepArchitect: Automatically Designing and Training Deep Architectures
Renato Negrinho and Geoffrey J. Gordon · 2017
Closest in time.
Large-Scale Evolution of Image Classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V. Le, and Alexey Kurakin · 2017
Closest in time.
Neural Architecture Search with Reinforcement Learning
Barret Zoph and Quoc V. Le · 2017
Closest in time.