The back-propagation algorithm is the cornerstone of deep learning.
Reading the bibliography…
The representation of the cumulative rounding error of an algorithm as a taylor expansion of the local rounding errors
Seppo Linnainmaa · 1970
Earlier work this paper cites.
Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences
Paul Werbos · 1974
Earlier work this paper cites.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
Earlier work this paper cites.
Use of genetic programming for the search of a new learning rule for neural networks
Samy Bengio, Yoshua Bengio, and Jocelyn Cloutier · 1994
Earlier work this paper cites.
Random feedback weights support learning in deep neural networks
Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, and Colin J Akerman · 2014
Earlier work this paper cites.
Keras, 2015
François Chollet et al · 2015
Earlier work this paper cites.
Difference target propagation
Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, and Yoshua Bengio · 2015
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Cited alongside, same era.
How important is weight symmetry in backpropagation?
Qianli Liao, Joel Z Leibo, and Tomaso A Poggio · 2016
Cited alongside, same era.
Sgdr: stochastic gradient descent with restarts
Ilya Loshchilov and Frank Hutter · 2016
Cited alongside, same era.
Direct feedback alignment provides learning in deep neural networks
Arild Nøkland · 2016
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2017
Cited alongside, same era.
Neural optimizer search with reinforcement learning
Irwan Bello, Barret Zoph, Vijay Vasudevan, and Quoc V Le · 2017
Cited alongside, same era.
Understanding and simplifying one-shot architecture search
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc Le
Cited in the paper.
Demystifying one-shot architecture search
Gabriel Bender, Pieter-jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc V Le
Cited in the paper.
SMASH: one-shot model architecture search through hypernetworks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 2017
Later among the works it cites.
Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2017
Later among the works it cites.
Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, and Alex Kurakin · 2017
Later among the works it cites.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2017
Later among the works it cites.
Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2018
Closest in time.
Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2018
Closest in time.
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…