Fetching the paper…
Reading the bibliography…
We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs.
Online learning and online convex optimization
Shai Shalev-Shwartz · 1935
Earlier work this paper cites.
A method of solving a convex programming problem with convergence rate o (1/k2)
Yurii Nesterov · 1983
Earlier work this paper cites.
Game theory, on-line prediction and boosting
Yoav Freund and Robert E. Schapire · 1996
Earlier work this paper cites.
Adaptive game playing using multiplicative weights
Yoav Freund and Robert E. Schapire · 1999
Earlier work this paper cites.
Dna binding sites: representation and discovery
Gary D Stormo · 2000
Earlier work this paper cites.
Efficient algorithms for online decision problems
Adam Kalai and Santosh Vempala · 2003
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Predicting the sequence specificities of dna-and rna-binding proteins by deep learning
Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey · 2015
Cited alongside, same era.
Mit 18.657: Mathematics of machine learning, lecture 16
Philippe Rigollet · 2015
Cited alongside, same era.
Fast convergence of regularized learning in games
Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, and Robert E Schapire · 2015
Cited alongside, same era.
Gerv: a statistical method for generative evaluation of regulatory variants for transcription factor binding
Haoyang Zeng, Tatsunori Hashimoto, Daniel D Kang, and David K Gifford · 2015
Cited alongside, same era.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Convolutional neural network architectures for predicting dna–protein binding
Haoyang Zeng, Matthew D Edwards, Ge Liu, and David K Gifford · 2016
Later among the works it cites.
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Closest in time.
NIPS 2016 tutorial: Generative adversarial networks
Ian J. Goodfellow · 2017
Closest in time.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martín Arjovsky, Vincent Dumoulin, and Aaron C. Courville · 2017
Closest in time.
Cycles in adversarial regularized learning
Panayotis Mertikopoulos, Christos Papadimitriou, and Georgios Piliouras · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Online learning with predictable sequences
Alexander Rakhlin and Karthik Sridharan
Cited in the paper.
Optimization, learning, and games with predictable sequences
Alexander Rakhlin and Karthik Sridharan
Cited in the paper.
Closest in time.
Predicting the impact of non-coding variants on dna methylation
Haoyang Zeng and David K Gifford · 2017
Closest in time.