Andriy Mnih - RankMe

61 papers A* 16A 3B 2Journal 26Unranked 13

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Uncertainty evaluation of segmentation models for Earth observation. CoRR Mélanie Rey, Andriy Mnih, Maxim Neumann, Matt Overlan, Drew Purves
2024	A*	`conf`	Schrodinger Bridge Flow for Unpaired Data Translation. NeurIPS Valentin De Bortoli, Iryna Korshunova, Andriy Mnih, Arnaud Doucet
2024	J	`jnl`	Schrödinger Bridge Flow for Unpaired Data Translation. CoRR Valentin De Bortoli, Iryna Korshunova, Andriy Mnih, Arnaud Doucet
2023	A*	`conf`	Compositional Score Modeling for Simulation-Based Inference. ICML Tomas Geffner, George Papamakarios, Andriy Mnih
2022	J	`jnl`	Score Modeling for Simulation-based Inference. CoRR Tomas Geffner, George Papamakarios, Andriy Mnih
2021	A*	`conf`	Coupled Gradient Estimators for Discrete Latent Variables. NeurIPS Zhe Dong, Andriy Mnih, George Tucker
2021	J	`jnl`	Coupled Gradient Estimators for Discrete Latent Variables. CoRR Zhe Dong, Andriy Mnih, George Tucker
2021	A*	`conf`	Generalized Doubly Reparameterized Gradient Estimators. ICML Matthias Bauer, Andriy Mnih
2021	J	`jnl`	Generalized Doubly Reparameterized Gradient Estimators. CoRR Matthias Bauer, Andriy Mnih
2021	A*	`conf`	The Lipschitz Constant of Self-Attention. ICML Hyunjik Kim, George Papamakarios, Andriy Mnih
2021	J	`jnl`	Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts. CoRR Wouter Kool, Chris J. Maddison, Andriy Mnih
2020	A*	`conf`	DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. NeurIPS Zhe Dong, Andriy Mnih, George Tucker
2020	J	`jnl`	DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. CoRR Zhe Dong, Andriy Mnih, George Tucker
2020	J	`jnl`	Monte Carlo Gradient Estimation in Machine Learning. J. Mach. Learn. Res. Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih
2020	J	`jnl`	Q-Learning in enormous action spaces via amortized approximate maximization. CoRR Tom Van de Wiele, David Warde-Farley, Andriy Mnih, Volodymyr Mnih
2020	A	`conf`	Sparse Orthogonal Variational Inference for Gaussian Processes. AISTATS Jiaxin Shi, Michalis K. Titsias, Andriy Mnih
2020	J	`jnl`	The Lipschitz Constant of Self-Attention. CoRR Hyunjik Kim, George Papamakarios, Andriy Mnih
2019	—	`conf`	Attentive Neural Processes. ICLR (Poster) Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, S. M. Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh
2019	J	`jnl`	Attentive Neural Processes. CoRR Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, S. M. Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh
2019	J	`jnl`	Monte Carlo Gradient Estimation in Machine Learning. CoRR Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih
2019	A	`conf`	Resampled Priors for Variational Autoencoders. AISTATS Matthias Bauer, Andriy Mnih
2019	J	`jnl`	Sparse Orthogonal Variational Inference for Gaussian Processes. CoRR Jiaxin Shi, Michalis K. Titsias, Andriy Mnih
2018	A*	`conf`	Disentangling by Factorising. ICML Hyunjik Kim, Andriy Mnih
2018	J	`jnl`	Disentangling by Factorising. CoRR Hyunjik Kim, Andriy Mnih
2018	A*	`conf`	Implicit Reparameterization Gradients. NeurIPS Mikhail Figurnov, Shakir Mohamed, Andriy Mnih
2018	J	`jnl`	Implicit Reparameterization Gradients. CoRR Michael Figurnov, Shakir Mohamed, Andriy Mnih
2018	J	`jnl`	Resampled Priors for Variational Autoencoders. CoRR Matthias Bauer, Andriy Mnih
2017	—	`conf`	Filtering Variational Objectives. NIPS Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
2017	J	`jnl`	Filtering Variational Objectives. CoRR Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
2017	—	`conf`	Particle Value Functions. ICLR (Workshop) Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
2017	J	`jnl`	Particle Value Functions. CoRR Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
2017	—	`conf`	REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. ICLR (Workshop) George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein
2017	—	`conf`	REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. NIPS George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein
2017	J	`jnl`	REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. CoRR George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein
2017	—	`conf`	The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. ICLR (Poster) Chris J. Maddison, Andriy Mnih, Yee Whye Teh
2017	—	`conf`	Variational Memory Addressing in Generative Models. NIPS Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende
2017	J	`jnl`	Variational Memory Addressing in Generative Models. CoRR Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende
2016	A*	`conf`	MuProp: Unbiased Backpropagation for Stochastic Neural Networks. ICLR (Poster) Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih
2016	J	`jnl`	The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. CoRR Chris J. Maddison, Andriy Mnih, Yee Whye Teh
2016	A*	`conf`	Variational Inference for Monte Carlo Objectives. ICML Andriy Mnih, Danilo Jimenez Rezende
2016	J	`jnl`	Variational inference for Monte Carlo objectives. CoRR Andriy Mnih, Danilo Jimenez Rezende
2014	A*	`conf`	Deep AutoRegressive Networks. ICML Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra
2014	A*	`conf`	Neural Variational Inference and Learning in Belief Networks. ICML Andriy Mnih, Karol Gregor
2014	J	`jnl`	Neural Variational Inference and Learning in Belief Networks. CoRR Andriy Mnih, Karol Gregor
2013	J	`jnl`	Deep AutoRegressive Networks. CoRR Karol Gregor, Andriy Mnih, Daan Wierstra
2013	—	`conf`	Learning word embeddings efficiently with noise-contrastive estimation. NIPS Andriy Mnih, Koray Kavukcuoglu
2012	A*	`conf`	A fast and simple algorithm for training neural probabilistic language models. ICML Andriy Mnih, Yee Whye Teh
2012	—	`conf`	Learning Label Trees for Probabilistic Modelling of Implicit Feedback. NIPS Andriy Mnih, Yee Whye Teh
2012	—	`conf`	Taxonomy-Informed Latent Factor Models for Implicit Feedback. KDD Cup Andriy Mnih
2011	J	`jnl`	Learning Item Trees for Probabilistic Modelling of Implicit Feedback CoRR Andriy Mnih, Yee Whye Teh
2010	—	`—`	Learning Distributed Representations for Statistical Language Modelling and Collaborative Filtering. Andriy Mnih
2009	J	`jnl`	Improving a statistical language model through non-linear prediction. Neurocomputing Andriy Mnih, Zhang Yuecheng, Geoffrey E. Hinton
2008	—	`conf`	A Scalable Hierarchical Distributed Language Model. NIPS Andriy Mnih, Geoffrey E. Hinton
2008	A*	`conf`	Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. ICML Ruslan Salakhutdinov, Andriy Mnih
2008	B	`conf`	Improving a statistical language model by modulating the effects of context words. ESANN Zhang Yuecheng, Andriy Mnih, Geoffrey E. Hinton
2007	—	`conf`	Probabilistic Matrix Factorization. NIPS Ruslan Salakhutdinov, Andriy Mnih
2007	A*	`conf`	Restricted Boltzmann machines for collaborative filtering. ICML Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton
2007	A*	`conf`	Three new graphical models for statistical language modelling. ICML Andriy Mnih, Geoffrey E. Hinton
2007	A	`conf`	Visualizing Similarity Data with a Mixture of Maps. AISTATS James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey E. Hinton
2005	B	`conf`	Learning nonlinear constraints with contrastive backpropagation. IJCNN Andriy Mnih, Geoffrey E. Hinton
2003	—	`conf`	Wormholes Improve Contrastive Divergence. NIPS Geoffrey E. Hinton, Max Welling, Andriy Mnih