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Andriy Mnih

University College London

Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts


Sep 24, 2021
Wouter Kool, Chris J. Maddison, Andriy Mnih


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Coupled Gradient Estimators for Discrete Latent Variables


Jun 15, 2021
Zhe Dong, Andriy Mnih, George Tucker

* Under Review 

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Generalized Doubly Reparameterized Gradient Estimators


Jan 26, 2021
Matthias Bauer, Andriy Mnih


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DisARM: An Antithetic Gradient Estimator for Binary Latent Variables


Jun 18, 2020
Zhe Dong, Andriy Mnih, George Tucker


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The Lipschitz Constant of Self-Attention


Jun 08, 2020
Hyunjik Kim, George Papamakarios, Andriy Mnih


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Q-Learning in enormous action spaces via amortized approximate maximization


Jan 22, 2020
Tom Van de Wiele, David Warde-Farley, Andriy Mnih, Volodymyr Mnih

* A previous version of this work appeared at the Deep Reinforcement Learning Workshop, NeurIPS 2018 

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Sparse Orthogonal Variational Inference for Gaussian Processes


Oct 24, 2019
Jiaxin Shi, Michalis K. Titsias, Andriy Mnih


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Monte Carlo Gradient Estimation in Machine Learning


Jun 25, 2019
Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih

* 59 pages, under review 

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Attentive Neural Processes


Jan 17, 2019
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh


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Implicit Reparameterization Gradients


Nov 01, 2018
Michael Figurnov, Shakir Mohamed, Andriy Mnih

* NIPS 2018 

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Resampled Priors for Variational Autoencoders


Oct 26, 2018
Matthias Bauer, Andriy Mnih


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Disentangling by Factorising


Jun 06, 2018
Hyunjik Kim, Andriy Mnih

* Shorter version appeared in Learning Disentangled Representations: From Perception to Control workshop at NIPS, 2017: https://sites.google.com/corp/view/disentanglenips2017 

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Filtering Variational Objectives


Nov 12, 2017
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh


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REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models


Nov 06, 2017
George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein

* NIPS 2017 

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Variational Memory Addressing in Generative Models


Sep 21, 2017
Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo J. Rezende


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Particle Value Functions


Mar 16, 2017
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh


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The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables


Mar 05, 2017
Chris J. Maddison, Andriy Mnih, Yee Whye Teh


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Variational inference for Monte Carlo objectives


Jun 01, 2016
Andriy Mnih, Danilo J. Rezende

* Appears in Proceedings of the 33rd International Conference on Machine Learning (ICML), New York, NY, USA, 2016. JMLR: W&CP volume 48 

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MuProp: Unbiased Backpropagation for Stochastic Neural Networks


Feb 25, 2016
Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih

* Published as a conference paper at ICLR 2016 

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Neural Variational Inference and Learning in Belief Networks


Jun 04, 2014
Andriy Mnih, Karol Gregor

* Proceedings of the 31st International Conference on Machine Learning (ICML), JMLR: W&CP volume 32, 2014 pgs 1791-1799 

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Deep AutoRegressive Networks


May 20, 2014
Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra

* Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra. Deep AutoRegressive Networks. In Proceedings of the 31st International Conference on Machine Learning (ICML), JMLR: W&CP volume 32, 2014 
* Appears in Proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, China, 2014 

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A Fast and Simple Algorithm for Training Neural Probabilistic Language Models


Jun 27, 2012
Andriy Mnih, Yee Whye Teh

* In Proceedings of the 29th International Conference on Machine Learning, pages 1751-1758, 2012 
* Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012) 

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Learning Item Trees for Probabilistic Modelling of Implicit Feedback


Sep 27, 2011
Andriy Mnih, Yee Whye Teh

* 8 pages 

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