Andriy Mnih

According to our database, Andriy Mnih authored at least 40 papers between 2003 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Compositional Score Modeling for Simulation-Based Inference.

Tomas Geffner, George Papamakarios, Andriy Mnih

Proceedings of the International Conference on Machine Learning, 2023

2022

Score Modeling for Simulation-based Inference.

Tomas Geffner, George Papamakarios, Andriy Mnih

CoRR, 2022

2021

Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts.

Wouter Kool, Chris J. Maddison, Andriy Mnih

CoRR, 2021

Coupled Gradient Estimators for Discrete Latent Variables.

Zhe Dong, Andriy Mnih, George Tucker

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Lipschitz Constant of Self-Attention.

Hyunjik Kim, George Papamakarios, Andriy Mnih

Proceedings of the 38th International Conference on Machine Learning, 2021

Generalized Doubly Reparameterized Gradient Estimators.

Matthias Bauer, Andriy Mnih

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Monte Carlo Gradient Estimation in Machine Learning.

Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih

J. Mach. Learn. Res., 2020

Q-Learning in enormous action spaces via amortized approximate maximization.

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

CoRR, 2020

DisARM: An Antithetic Gradient Estimator for Binary Latent Variables.

Zhe Dong, Andriy Mnih, George Tucker

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Sparse Orthogonal Variational Inference for Gaussian Processes.

Jiaxin Shi, Michalis K. Titsias, Andriy Mnih

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Attentive Neural Processes.

Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, S. M. Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh

Proceedings of the 7th International Conference on Learning Representations, 2019

Resampled Priors for Variational Autoencoders.

Matthias Bauer, Andriy Mnih

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Implicit Reparameterization Gradients.

Mikhail Figurnov, Shakir Mohamed, Andriy Mnih

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Disentangling by Factorising.

Hyunjik Kim, Andriy Mnih

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

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

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

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Filtering Variational Objectives.

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

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Memory Addressing in Generative Models.

Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

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

George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein

Proceedings of the 5th International Conference on Learning Representations, 2017

The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

Chris J. Maddison, Andriy Mnih, Yee Whye Teh

Proceedings of the 5th International Conference on Learning Representations, 2017

Particle Value Functions.

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

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

MuProp: Unbiased Backpropagation for Stochastic Neural Networks.

Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih

Proceedings of the 4th International Conference on Learning Representations, 2016

Variational Inference for Monte Carlo Objectives.

Andriy Mnih, Danilo Jimenez Rezende

Proceedings of the 33nd International Conference on Machine Learning, 2016

2014

Neural Variational Inference and Learning in Belief Networks.

Andriy Mnih, Karol Gregor

Proceedings of the 31th International Conference on Machine Learning, 2014

Deep AutoRegressive Networks.

Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Deep AutoRegressive Networks.

Karol Gregor, Andriy Mnih, Daan Wierstra

CoRR, 2013

Learning word embeddings efficiently with noise-contrastive estimation.

Andriy Mnih, Koray Kavukcuoglu

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Taxonomy-Informed Latent Factor Models for Implicit Feedback.

Andriy Mnih

Proceedings of KDD Cup 2011 competition, San Diego, CA, USA, 2011, 2012

Learning Label Trees for Probabilistic Modelling of Implicit Feedback.

Andriy Mnih, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

A fast and simple algorithm for training neural probabilistic language models.

Andriy Mnih, Yee Whye Teh

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Learning Item Trees for Probabilistic Modelling of Implicit Feedback

Andriy Mnih, Yee Whye Teh

CoRR, 2011

2010

Learning Distributed Representations for Statistical Language Modelling and Collaborative Filtering.

Andriy Mnih

PhD thesis, 2010

2009

Improving a statistical language model through non-linear prediction.

Andriy Mnih, Zhang Yuecheng, Geoffrey E. Hinton

Neurocomputing, 2009

2008

A Scalable Hierarchical Distributed Language Model.

Andriy Mnih, Geoffrey E. Hinton

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Bayesian probabilistic matrix factorization using Markov chain Monte Carlo.

Ruslan Salakhutdinov, Andriy Mnih

Proceedings of the Machine Learning, 2008

Improving a statistical language model by modulating the effects of context words.

Zhang Yuecheng, Andriy Mnih, Geoffrey E. Hinton

Proceedings of the 16th European Symposium on Artificial Neural Networks, 2008

2007

Visualizing Similarity Data with a Mixture of Maps.

James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey E. Hinton

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Probabilistic Matrix Factorization.

Ruslan Salakhutdinov, Andriy Mnih

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Restricted Boltzmann machines for collaborative filtering.

Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton

Proceedings of the Machine Learning, 2007

Three new graphical models for statistical language modelling.

Andriy Mnih, Geoffrey E. Hinton

Proceedings of the Machine Learning, 2007

2003

Wormholes Improve Contrastive Divergence.

Geoffrey E. Hinton, Max Welling, Andriy Mnih

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003