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Adversarial Divergences are Good Task Losses for Generative Modeling.

G. Huang, G. Gidel, H. Berard, A. Touati, and S. Lacoste-Julien. CoRR, (2017)

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Berard Hugo Wingenfeld

Mechthild Berard

François Bérard

Peter Berard

Berard Wingenfeld

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Parametric Adversarial Divergences are Good Task Losses for Generative Modeling.G. Huang, H. Berard, A. Touati, G. Gidel, P. Vincent, and S. Lacoste-Julien. ICLR (Workshop), OpenReview.net, (2018)Adversarial Divergences are Good Task Losses for Generative Modeling.G. Huang, G. Gidel, H. Berard, A. Touati, and S. Lacoste-Julien. CoRR, (2017)A Closer Look at the Optimization Landscapes of Generative Adversarial NetworksH. Berard, G. Gidel, A. Almahairi, P. Vincent, and S. Lacoste-Julien. (2019)cite arxiv:1906.04848.Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity.N. Loizou, H. Berard, G. Gidel, I. Mitliagkas, and S. Lacoste-Julien. NeurIPS, page 19095-19108. (2021)Adversarial Example Games.A. Bose, G. Gidel, H. Berard, A. Cianflone, P. Vincent, S. Lacoste-Julien, and W. Hamilton. NeurIPS, (2020)A Variational Inequality Perspective on Generative Adversarial Nets.G. Gidel, H. Berard, P. Vincent, and S. Lacoste-Julien. CoRR, (2018)A Variational Inequality Perspective on Generative Adversarial Networks.G. Gidel, H. Berard, G. Vignoud, P. Vincent, and S. Lacoste-Julien. ICLR (Poster), OpenReview.net, (2019)Stochastic Extragradient: General Analysis and Improved Rates.E. Gorbunov, H. Berard, G. Gidel, and N. Loizou. AISTATS, volume 151 of Proceedings of Machine Learning Research, page 7865-7901. PMLR, (2022)Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods.A. Beznosikov, E. Gorbunov, H. Berard, and N. Loizou. AISTATS, volume 206 of Proceedings of Machine Learning Research, page 172-235. PMLR, (2023)Stochastic Hamiltonian Gradient Methods for Smooth Games.N. Loizou, H. Berard, A. Jolicoeur-Martineau, P. Vincent, S. Lacoste-Julien, and I. Mitliagkas. ICML, volume 119 of Proceedings of Machine Learning Research, page 6370-6381. PMLR, (2020)

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Disambiguation of "Berard, Hugo"

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Adversarial Divergences are Good Task Losses for Generative Modeling.

Please choose a person to relate this publication to

Berard Hugo Wingenfeld

Mechthild Berard

François Bérard

Peter Berard

Berard Wingenfeld

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Berard, Hugo"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Adversarial Divergences are Good Task Losses for Generative Modeling.

Please choose a person to relate this publication to

Berard Hugo Wingenfeld

Mechthild Berard

François Bérard

Peter Berard

Berard Wingenfeld

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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Adversarial Divergences are Good Task Losses for Generative Modeling.