Mnih, Andriy

34 publications

NeurIPS 2024 Schrodinger Bridge Flow for Unpaired Data Translation Valentin De Bortoli, Iryna Korshunova, Andriy Mnih, Arnaud Doucet

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

NeurIPSW 2022 Score Modeling for Simulation-Based Inference Tomas Geffner, George Papamakarios, Andriy Mnih

NeurIPS 2021 Coupled Gradient Estimators for Discrete Latent Variables Zhe Dong, Andriy Mnih, George Tucker

ICML 2021 Generalized Doubly Reparameterized Gradient Estimators Matthias Bauer, Andriy Mnih

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

NeurIPSW 2021 Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts Wouter Kool, Chris J. Maddison, Andriy Mnih

NeurIPS 2020 DisARM: An Antithetic Gradient Estimator for Binary Latent Variables Zhe Dong, Andriy Mnih, George Tucker

JMLR 2020 Monte Carlo Gradient Estimation in Machine Learning Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih

AISTATS 2020 Sparse Orthogonal Variational Inference for Gaussian Processes Jiaxin Shi, Michalis Titsias, Andriy Mnih

ICLR 2019 Attentive Neural Processes Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh

AISTATS 2019 Resampled Priors for Variational Autoencoders Matthias Bauer, Andriy Mnih

ICML 2018 Disentangling by Factorising Hyunjik Kim, Andriy Mnih

NeurIPS 2018 Implicit Reparameterization Gradients Mikhail Figurnov, Shakir Mohamed, Andriy Mnih

NeurIPS 2017 Filtering Variational Objectives Chris J Maddison, John Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Teh

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

NeurIPS 2017 REBAR: Low-Variance, Unbiased Gradient Estimates for Discrete Latent Variable Models George Tucker, Andriy Mnih, Chris J Maddison, John Lawson, Jascha Sohl-Dickstein

ICLR 2017 REBAR: Low-Variance, Unbiased Gradient Estimates for Discrete Latent Variable Models George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein

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

NeurIPS 2017 Variational Memory Addressing in Generative Models Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende

ICLR 2016 MuProp: Unbiased Backpropagation for Stochastic Neural Networks Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih

ICML 2016 Variational Inference for Monte Carlo Objectives Andriy Mnih, Danilo Rezende

ICML 2014 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

NeurIPS 2013 Learning Word Embeddings Efficiently with Noise-Contrastive Estimation Andriy Mnih, Koray Kavukcuoglu

ICML 2012 A Fast and Simple Algorithm for Training Neural Probabilistic Language Models Andriy Mnih, Yee Whye Teh

NeurIPS 2012 Learning Label Trees for Probabilistic Modelling of Implicit Feedback Andriy Mnih, Yee W. Teh

NeurIPS 2008 A Scalable Hierarchical Distributed Language Model Andriy Mnih, Geoffrey E. Hinton

ICML 2008 Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo Ruslan Salakhutdinov, Andriy Mnih

NeurIPS 2007 Probabilistic Matrix Factorization Andriy Mnih, Ruslan Salakhutdinov

ICML 2007 Restricted Boltzmann Machines for Collaborative Filtering Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton

ICML 2007 Three New Graphical Models for Statistical Language Modelling Andriy Mnih, Geoffrey E. Hinton

AISTATS 2007 Visualizing Similarity Data with a Mixture of Maps James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey Hinton

NeurIPS 2003 Wormholes Improve Contrastive Divergence Max Welling, Andriy Mnih, Geoffrey E. Hinton