Wormholes Improve Contrastive Divergence. - Geoffrey E. Hinton; Max Welling; Andriy Mnih (NIPS, 2003)

Learning nonlinear constraints with contrastive backpropagation. - Andriy Mnih; Geoffrey E. Hinton (IJCNN, 2005)

Three new graphical models for statistical language modelling. - Andriy Mnih; Geoffrey E. Hinton (ICML, 2007)

Restricted Boltzmann machines for collaborative filtering. - Ruslan Salakhutdinov; Andriy Mnih; Geoffrey E. Hinton (ICML, 2007)

Probabilistic Matrix Factorization. - Ruslan Salakhutdinov; Andriy Mnih (NIPS, 2007)

Visualizing Similarity Data with a Mixture of Maps. - James Cook; Ilya Sutskever; Andriy Mnih; Geoffrey E. Hinton (AISTATS, 2007)

Improving a statistical language model by modulating the effects of context words. - Zhang Yuecheng; Andriy Mnih; Geoffrey E. Hinton (ESANN, 2008)

Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. - Ruslan Salakhutdinov; Andriy Mnih (ICML, 2008)

A Scalable Hierarchical Distributed Language Model. - Andriy Mnih; Geoffrey E. Hinton (NIPS, 2008)

Improving a statistical language model through non-linear prediction. - Andriy Mnih; Zhang Yuecheng; Geoffrey E. Hinton (Neurocomputing, 2009)

Learning Distributed Representations for Statistical Language Modelling and Collaborative Filtering. - Andriy Mnih (2010)

Learning Item Trees for Probabilistic Modelling of Implicit Feedback - Andriy Mnih; Yee Whye Teh (CoRR, 2011)

A fast and simple algorithm for training neural probabilistic language models. - Andriy Mnih; Yee Whye Teh (ICML, 2012)

Learning Label Trees for Probabilistic Modelling of Implicit Feedback. - Andriy Mnih; Yee Whye Teh (NIPS, 2012)

Taxonomy-Informed Latent Factor Models for Implicit Feedback. - Andriy Mnih (KDD Cup, 2012)

Learning word embeddings efficiently with noise-contrastive estimation. - Andriy Mnih; Koray Kavukcuoglu (NIPS, 2013)

Deep AutoRegressive Networks. - Karol Gregor; Andriy Mnih; Daan Wierstra (CoRR, 2013)

Deep AutoRegressive Networks. - Karol Gregor; Ivo Danihelka; Andriy Mnih; Charles Blundell; Daan Wierstra (ICML, 2014)

Neural Variational Inference and Learning in Belief Networks. - Andriy Mnih; Karol Gregor (ICML, 2014)

Variational Inference for Monte Carlo Objectives. - Andriy Mnih; Danilo Jimenez Rezende (ICML, 2016)

MuProp: Unbiased Backpropagation for Stochastic Neural Networks. - Shixiang Gu; Sergey Levine; Ilya Sutskever; Andriy Mnih (ICLR (Poster), 2016)

Particle Value Functions. - Chris J. Maddison; Dieterich Lawson; George Tucker; Nicolas Heess; Arnaud Doucet; Andriy Mnih; Yee Whye Teh (ICLR (Workshop), 2017)

The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. - Chris J. Maddison; Andriy Mnih; Yee Whye Teh (ICLR (Poster), 2017)

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. - George Tucker; Andriy Mnih; Chris J. Maddison; Jascha Sohl-Dickstein (ICLR (Workshop), 2017)

Variational Memory Addressing in Generative Models. - Jörg Bornschein; Andriy Mnih; Daniel Zoran; Danilo Jimenez Rezende (NIPS, 2017)

Filtering Variational Objectives. - Chris J. Maddison; Dieterich Lawson; George Tucker; Nicolas Heess; Mohammad Norouzi 002; Andriy Mnih; Arnaud Doucet; Yee Whye Teh (NIPS, 2017)

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)

Disentangling by Factorising. - Hyunjik Kim; Andriy Mnih (ICML, 2018)

Implicit Reparameterization Gradients. - Mikhail Figurnov; Shakir Mohamed; Andriy Mnih (NeurIPS, 2018)

Resampled Priors for Variational Autoencoders. - Matthias Bauer; Andriy Mnih (AISTATS, 2019)

Attentive Neural Processes. - Hyunjik Kim; Andriy Mnih; Jonathan Schwarz; Marta Garnelo; S. M. Ali Eslami; Dan Rosenbaum; Oriol Vinyals; Yee Whye Teh (ICLR (Poster), 2019)

Sparse Orthogonal Variational Inference for Gaussian Processes. - Jiaxin Shi; Michalis K. Titsias; Andriy Mnih (AISTATS, 2020)

DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. - Zhe Dong; Andriy Mnih; George Tucker (NeurIPS, 2020)

Q-Learning in enormous action spaces via amortized approximate maximization. - Tom Van de Wiele; David Warde-Farley; Andriy Mnih; Volodymyr Mnih (CoRR, 2020)

Monte Carlo Gradient Estimation in Machine Learning. - Shakir Mohamed; Mihaela Rosca; Michael Figurnov; Andriy Mnih (J. Mach. Learn. Res., 2020)

Generalized Doubly Reparameterized Gradient Estimators. - Matthias Bauer; Andriy Mnih (ICML, 2021)

The Lipschitz Constant of Self-Attention. - Hyunjik Kim; George Papamakarios; Andriy Mnih (ICML, 2021)

Coupled Gradient Estimators for Discrete Latent Variables. - Zhe Dong; Andriy Mnih; George Tucker (NeurIPS, 2021)

Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts. - Wouter Kool 001; Chris J. Maddison; Andriy Mnih (CoRR, 2021)

Score Modeling for Simulation-based Inference. - Tomas Geffner; George Papamakarios; Andriy Mnih (CoRR, 2022)

Compositional Score Modeling for Simulation-Based Inference. - Tomas Geffner; George Papamakarios; Andriy Mnih (ICML, 2023)

Schrodinger Bridge Flow for Unpaired Data Translation. - Valentin De Bortoli; Iryna Korshunova; Andriy Mnih; Arnaud Doucet (NeurIPS, 2024)