Andriy Mnih (original) (raw)
January 2020 [arxiv]
Sparse Orthogonal Variational Inference for Gaussian Processes Jiaxin Shi, Michalis K. Titsias, Andriy Mnih AISTATS 2020 [arxiv]
Measure-Valued Derivatives for Approximate Bayesian Inference Mihaela Rosca, Michael Figurnov, Shakir Mohamed, Andriy Mnih Bayesian Deep Learning Workshop, NeurIPS 2019 [pdf]
Attentive Neural Processes Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh ICLR 2019 [openreview] [arxiv]
Resampled Priors for Variational Autoencoders Matthias Bauer, Andriy Mnih AISTATS 2019 [arxiv]
Implicit Reparameterization Gradients Michael Figurnov, Shakir Mohamed, Andriy Mnih NeurIPS 2018 [NeurIPS] [arxiv]
Disentangling by Factorising Hyunjik Kim, Andriy Mnih ICML 2018 [arxiv] [pdf (workshop version)]
Continuous Relaxation Training of Discrete Latent Variable Image Models Casper Kaae Sønderby, Ben Poole, Andriy Mnih Bayesian Deep Learning Workshop, NIPS 2017 [pdf]
Variational Memory Addressing in Generative Models Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo J. Rezende NIPS 2017 [arxiv]
REBAR : Low-variance, unbiased gradient estimates for discrete latent variable models George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein NIPS 2017 [arxiv]
Filtering Variational Objectives Chris J. Maddison*, Dieterich Lawson*, George Tucker*, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh * denotes equal contribution NIPS 2017 [arxiv]
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables Chris J. Maddison, Andriy Mnih, Yee Whye Teh ICLR 2017 [arxiv]
Variational inference for Monte Carlo objectives Andriy Mnih and Danilo J. Rezende ICML 2016 [arxiv] [slides] [poster] [bibtex]
MuProp: Unbiased Backpropagation for Stochastic Neural Networks Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih ICLR 2016 [arxiv]
Neural Variational Inference and Learning in Belief Networks Andriy Mnih and Karol Gregor ICML 2014 [pdf] [slides] [poster] [bibtex] [talk]
Deep AutoRegressive Networks Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra ICML 2014 [pdf] [bibtex]
Learning word embeddings efficiently with noise-contrastive estimation Andriy Mnih and Koray Kavukcuoglu NIPS 2013 [pdf] [poster] [bibtex]
Learning Label Trees for Probabilistic Modelling of Implicit Feedback Andriy Mnih and Yee Whye Teh NIPS 2012 [pdf] [poster] [bibtex]
A fast and simple algorithm for training neural probabilistic language models Andriy Mnih and Yee Whye Teh ICML 2012 [pdf] [slides] [poster] [bibtex] [5 min talk]
Taxonomy-Informed Latent Factor Models for Implicit Feedback Andriy Mnih JMLR W&CP Volume 18: Proceedings of KDD Cup 2011 [pdf] [slides] [bibtex]
Learning Distributed Representations for Statistical Language Modelling and Collaborative Filtering Andriy Mnih PhD Thesis, University of Toronto, 2009 [pdf] [bibtex]
Improving a Statistical Language Model Through Non-linear Prediction Andriy Mnih, Zhang Yuecheng, and Geoffrey Hinton Neurocomputing, 72:7-9, 2009 [bibtex]
A Scalable Hierarchical Distributed Language Model Andriy Mnih and Geoffrey Hinton NIPS 2008 [pdf] [bibtex]
Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo Ruslan Salakhutdinov and Andriy Mnih ICML 2008 [pdf] [bibtex]
Improving a Statistical Language Model by Modulating the Effects of Context Words Zhang Yuecheng, Andriy Mnih, and Geoffrey Hinton European Symposium on Artificial Neural Networks 2008 (ESANN 2008)
Probabilistic Matrix Factorization Ruslan Salakhutdinov and Andriy Mnih NIPS 2007 [pdf] [bibtex]
Three New Graphical Models for Statistical Language Modelling Andriy Mnih and Geoffrey Hinton ICML 2007 [pdf] [bibtex]
Restricted Boltzmann Machines for Collaborative Filtering Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton ICML 2007 [pdf] [bibtex]
Visualizing Similarity Data with a Mixture of Maps James Cook, Ilya Sutskever, Andriy Mnih, and Geoffrey Hinton AISTATS 2007 [pdf] [bibtex]
Learning Nonlinear Constraints with Contrastive Backpropagation Andriy Mnih and Geoffrey Hinton International Joint Conference on Neural Networks 2005 (IJCNN 2005) [bibtex]
Wormholes Improve Contrastive Divergence Geoffrey Hinton, Max Welling, and Andriy Mnih NIPS 2003 [bibtex]