Andriy Mnih
Andriy Mnih
Research Scientist at DeepMind
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
Probabilistic matrix factorization
R Salakhutdinov, A Mnih
Advances in neural information processing systems 20, 1257-1264, 2008
3542*2008
Restricted Boltzmann machines for collaborative filtering
R Salakhutdinov, A Mnih, G Hinton
Proceedings of the 24th international conference on Machine learning, 791-798, 2007
18112007
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
R Salakhutdinov, A Mnih
Proceedings of the 25th international conference on Machine learning, 880-887, 2008
13872008
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
R Salakhutdinov, A Mnih
Proceedings of the 25th international conference on Machine learning, 880-887, 2008
13772008
A scalable hierarchical distributed language model
A Mnih, GE Hinton
Advances in Neural Information Processing Systems 21, 1081-1088, 2009
10092009
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
CJ Maddison, A Mnih, YW Teh
International Conference on Learning Representations 2017, 2016
9012016
Three new graphical models for statistical language modelling
A Mnih, G Hinton
Proceedings of the 24th international conference on Machine learning, 641-648, 2007
6482007
Neural Variational Inference and Learning in Belief Networks
A Mnih, K Gregor
International Conference on Machine Learning 2014, 2014
5442014
A fast and simple algorithm for training neural probabilistic language models
A Mnih, YW Teh
International Conference on Machine Learning 2012, 2012
4842012
Learning word embeddings efficiently with noise-contrastive estimation
A Mnih, K Kavukcuoglu
Advances in Neural Information Processing Systems, 2265-2273, 2013
4832013
Disentangling by factorising
H Kim, A Mnih
International Conference on Machine Learning 2018, 2018
3102018
Deep autoregressive networks
K Gregor, I Danihelka, A Mnih, C Blundell, D Wierstra
Proceedings of the 31st International Conference on Machine Learning (ICML …, 2014
1952014
Variational inference for Monte Carlo objectives
A Mnih, DJ Rezende
International Conference on Machine Learning 2016, 2016
1762016
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
G Tucker, A Mnih, CJ Maddison, J Lawson, J Sohl-Dickstein
Advances in Neural Information Processing Systems, 2624-2633, 2017
1702017
MuProp: Unbiased Backpropagation for Stochastic Neural Networks
S Gu, S Levine, I Sutskever, A Mnih
International Conference on Learning Representations 2016, 2015
972015
Visualizing similarity data with a mixture of maps
J Cook, I Sutskever, A Mnih, G Hinton
Artificial Intelligence and Statistics, 67-74, 2007
972007
Filtering Variational Objectives
CJ Maddison, D Lawson, G Tucker, N Heess, M Norouzi, A Mnih, ...
Advances in Neural Information Processing Systems 2017, 2017
962017
Implicit reparameterization gradients
M Figurnov, S Mohamed, A Mnih
Advances in Neural Information Processing Systems, 441-452, 2018
792018
Attentive Neural Processes
H Kim, A Mnih, J Schwarz, M Garnelo, A Eslami, D Rosenbaum, O Vinyals, ...
International Conference on Learning Representations 2019, 2018
602018
Monte Carlo Gradient Estimation in Machine Learning
S Mohamed, M Rosca, M Figurnov, A Mnih
Journal of Machine Learning Research 21 (132), 1-62, 2020
402020
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Articles 1–20