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20172021
most citedVariational Approaches for Auto-Encoding Generative Adversarial Networks

248 citations · 306 across the 3 of their papers we have counts for

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stat.ML2021

Discretization Drift in Two-Player Games

Mihaela Rosca, Yan Wu, Benoit Dherin +1

Gradient-based methods for two-player games produce rich dynamics that can solve challenging problems, yet can be difficult to stabilize and understand. Part of this complexity ori…

stat.ML2020

A case for new neural network smoothness constraints

Mihaela Rosca, Theophane Weber, Arthur Gretton +1

How sensitive should machine learning models be to input changes? We tackle the question of model smoothness and show that it is a useful inductive bias which aids generalization,…

stat.ML2019

Monte Carlo Gradient Estimation in Machine Learning

Shakir Mohamed, Mihaela Rosca, Michael Figurnov +1

This paper is a broad and accessible survey of the methods we have at our disposal for Monte Carlo gradient estimation in machine learning and across the statistical sciences: the…

stat.ML2018

Distribution Matching in Variational Inference

Mihaela Rosca, Balaji Lakshminarayanan, Shakir Mohamed

With the increasingly widespread deployment of generative models, there is a mounting need for a deeper understanding of their behaviors and limitations. In this paper, we expose t…

stat.ML2017248 cited

Variational Approaches for Auto-Encoding Generative Adversarial Networks

Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley +1

Auto-encoding generative adversarial networks (GANs) combine the standard GAN algorithm, which discriminates between real and model-generated data, with a reconstruction loss given…