Martin Arjovsky
Martin Arjovsky
PhD student, Courant Institute of Mathematical Sciences, New York University
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Cited by
Cited by
Wasserstein gan
M Arjovsky, S Chintala, L Bottou
arXiv preprint arXiv:1701.07875, 2017
Improved training of wasserstein gans
I Gulrajani, F Ahmed, M Arjovsky, V Dumoulin, AC Courville
Advances in Neural Information Processing Systems, 5767-5777, 2017
Towards Principled Methods for Training Generative Adversarial Networks
M Arjovsky, L Bottou
arXiv preprint arXiv:1701.04862, 2017
Adversarially learned inference
V Dumoulin, I Belghazi, B Poole, O Mastropietro, A Lamb, M Arjovsky, ...
arXiv preprint arXiv:1606.00704, 2016
Unitary evolution recurrent neural networks
M Arjovsky, A Shah, Y Bengio
International Conference on Machine Learning, 1120-1128, 2016
Invariant Risk Minimization
M Arjovsky, L Bottou, I Gulrajani, D Lopez-Paz
arXiv preprint arXiv:1907.02893, 2019
Symplectic Recurrent Neural Networks
Z Chen, J Zhang, M Arjovsky, L Bottou
arXiv preprint arXiv:1909.13334, 2019
Geometrical insights for implicit generative modeling
L Bottou, M Arjovsky, D Lopez-Paz, M Oquab
Braverman Readings in Machine Learning. Key Ideas from Inception to Current …, 2018
Never Give Up: Learning Directed Exploration Strategies
AP Badia, P Sprechmann, A Vitvitskyi, D Guo, B Piot, S Kapturowski, ...
arXiv preprint arXiv:2002.06038, 2020
Optimizing transcoder quality targets using a neural network with an embedded bitrate model
M Covell, M Arjovsky, Y Lin, A Kokaram
Electronic Imaging 2016 (2), 1-7, 2016
Saddle-free Hessian-free optimization
M Arjovsky
arXiv preprint arXiv:1506.00059, 2015
Low Distortion Block-Resampling with Spatially Stochastic Networks
SJ Hong, M Arjovsky, I Thompson, D Barnhardt
arXiv preprint arXiv:2006.05394, 2020
Linear unit tests for invariance discovery
B Aubin, M Arjovsky, L Bottou, D Lopez-Paz
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