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ORCIDAndriy Mnih
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2020 – today
- 2024
[i23]Valentin De Bortoli, Iryna Korshunova, Andriy Mnih, Arnaud Doucet:
Schrödinger Bridge Flow for Unpaired Data Translation. CoRR abs/2409.09347 (2024)- 2023
- [c32]Tomas Geffner, George Papamakarios, Andriy Mnih:
Compositional Score Modeling for Simulation-Based Inference. ICML 2023: 11098-11116 - 2022
- [i22]Tomas Geffner, George Papamakarios, Andriy Mnih:
Score Modeling for Simulation-based Inference. CoRR abs/2209.14249 (2022) - 2021
- [c31]Matthias Bauer, Andriy Mnih:
Generalized Doubly Reparameterized Gradient Estimators. ICML 2021: 738-747 - [c30]Hyunjik Kim, George Papamakarios, Andriy Mnih:
The Lipschitz Constant of Self-Attention. ICML 2021: 5562-5571 - [c29]Zhe Dong, Andriy Mnih, George Tucker:
Coupled Gradient Estimators for Discrete Latent Variables. NeurIPS 2021: 24498-24508 - [i21]Matthias Bauer, Andriy Mnih:
Generalized Doubly Reparameterized Gradient Estimators. CoRR abs/2101.11046 (2021) - [i20]Zhe Dong
, Andriy Mnih, George Tucker:
Coupled Gradient Estimators for Discrete Latent Variables. CoRR abs/2106.08056 (2021) - [i19]Wouter Kool, Chris J. Maddison, Andriy Mnih:
Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts. CoRR abs/2109.11817 (2021) - 2020
- [j2]Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih:
Monte Carlo Gradient Estimation in Machine Learning. J. Mach. Learn. Res. 21: 132:1-132:62 (2020) - [c28]Jiaxin Shi, Michalis K. Titsias, Andriy Mnih:
Sparse Orthogonal Variational Inference for Gaussian Processes. AISTATS 2020: 1932-1942 - [c27]Zhe Dong, Andriy Mnih, George Tucker:
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. NeurIPS 2020 - [i18]Tom Van de Wiele, David Warde-Farley, Andriy Mnih, Volodymyr Mnih:
Q-Learning in enormous action spaces via amortized approximate maximization. CoRR abs/2001.08116 (2020) - [i17]Hyunjik Kim, George Papamakarios, Andriy Mnih:
The Lipschitz Constant of Self-Attention. CoRR abs/2006.04710 (2020) - [i16]Zhe Dong, Andriy Mnih, George Tucker:
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. CoRR abs/2006.10680 (2020)
2010 – 2019
- 2019
- [c26]Matthias Bauer, Andriy Mnih:
Resampled Priors for Variational Autoencoders. AISTATS 2019: 66-75 - [c25]Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, S. M. Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh:
Attentive Neural Processes. ICLR (Poster) 2019 - [i15]Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, S. M. Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh:
Attentive Neural Processes. CoRR abs/1901.05761 (2019) - [i14]Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih:
Monte Carlo Gradient Estimation in Machine Learning. CoRR abs/1906.10652 (2019) - [i13]Jiaxin Shi, Michalis K. Titsias, Andriy Mnih:
Sparse Orthogonal Variational Inference for Gaussian Processes. CoRR abs/1910.10596 (2019) - 2018
- [c24]Hyunjik Kim, Andriy Mnih:
Disentangling by Factorising. ICML 2018: 2654-2663 - [c23]Mikhail Figurnov, Shakir Mohamed, Andriy Mnih:
Implicit Reparameterization Gradients. NeurIPS 2018: 439-450 - [i12]Hyunjik Kim, Andriy Mnih:
Disentangling by Factorising. CoRR abs/1802.05983 (2018) - [i11]Michael Figurnov, Shakir Mohamed, Andriy Mnih:
Implicit Reparameterization Gradients. CoRR abs/1805.08498 (2018) - [i10]Matthias Bauer, Andriy Mnih:
Resampled Priors for Variational Autoencoders. CoRR abs/1810.11428 (2018) - 2017
- [c22]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh:
Particle Value Functions. ICLR (Workshop) 2017 - [c21]Chris J. Maddison, Andriy Mnih, Yee Whye Teh:
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. ICLR (Poster) 2017 - [c20]George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. ICLR (Workshop) 2017 - [c19]George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. NIPS 2017: 2627-2636 - [c18]Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende:
Variational Memory Addressing in Generative Models. NIPS 2017: 3920-3929 - [c17]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh:
Filtering Variational Objectives. NIPS 2017: 6573-6583 - [i9]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh:
Particle Value Functions. CoRR abs/1703.05820 (2017) - [i8]George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. CoRR abs/1703.07370 (2017) - [i7]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh:
Filtering Variational Objectives. CoRR abs/1705.09279 (2017) - [i6]Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende:
Variational Memory Addressing in Generative Models. CoRR abs/1709.07116 (2017) - 2016
- [c16]Andriy Mnih, Danilo Jimenez Rezende:
Variational Inference for Monte Carlo Objectives. ICML 2016: 2188-2196 - [c15]Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih:
MuProp: Unbiased Backpropagation for Stochastic Neural Networks. ICLR (Poster) 2016 - [i5]Andriy Mnih, Danilo Jimenez Rezende:
Variational inference for Monte Carlo objectives. CoRR abs/1602.06725 (2016) - [i4]Chris J. Maddison, Andriy Mnih, Yee Whye Teh:
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. CoRR abs/1611.00712 (2016) - 2014
- [c14]Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra:
Deep AutoRegressive Networks. ICML 2014: 1242-1250 - [c13]Andriy Mnih, Karol Gregor:
Neural Variational Inference and Learning in Belief Networks. ICML 2014: 1791-1799 - [i3]Andriy Mnih, Karol Gregor:
Neural Variational Inference and Learning in Belief Networks. CoRR abs/1402.0030 (2014) - 2013
- [c12]Andriy Mnih, Koray Kavukcuoglu:
Learning word embeddings efficiently with noise-contrastive estimation. NIPS 2013: 2265-2273 - [i2]Karol Gregor, Andriy Mnih, Daan Wierstra:
Deep AutoRegressive Networks. CoRR abs/1310.8499 (2013) - 2012
- [c11]Andriy Mnih, Yee Whye Teh:
A fast and simple algorithm for training neural probabilistic language models. ICML 2012 - [c10]Andriy Mnih, Yee Whye Teh:
Learning Label Trees for Probabilistic Modelling of Implicit Feedback. NIPS 2012: 2825-2833 - [c9]Andriy Mnih:
Taxonomy-Informed Latent Factor Models for Implicit Feedback. KDD Cup 2012: 169-181 - 2011
- [i1]Andriy Mnih, Yee Whye Teh:
Learning Item Trees for Probabilistic Modelling of Implicit Feedback. CoRR abs/1109.5894 (2011) - 2010
- [b1]Andriy Mnih:
Learning Distributed Representations for Statistical Language Modelling and Collaborative Filtering. University of Toronto, Canada, 2010
2000 – 2009
- 2009
- [j1]Andriy Mnih, Zhang Yuecheng, Geoffrey E. Hinton:
Improving a statistical language model through non-linear prediction. Neurocomputing 72(7-9): 1414-1418 (2009) - 2008
- [c8]Zhang Yuecheng, Andriy Mnih, Geoffrey E. Hinton:
Improving a statistical language model by modulating the effects of context words. ESANN 2008: 493-498 - [c7]Ruslan Salakhutdinov, Andriy Mnih:
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. ICML 2008: 880-887 - [c6]Andriy Mnih, Geoffrey E. Hinton:
A Scalable Hierarchical Distributed Language Model. NIPS 2008: 1081-1088 - 2007
- [c5]Andriy Mnih, Geoffrey E. Hinton:
Three new graphical models for statistical language modelling. ICML 2007: 641-648 - [c4]Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton:
Restricted Boltzmann machines for collaborative filtering. ICML 2007: 791-798 - [c3]Ruslan Salakhutdinov, Andriy Mnih:
Probabilistic Matrix Factorization. NIPS 2007: 1257-1264 - [c2]James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey E. Hinton:
Visualizing Similarity Data with a Mixture of Maps. AISTATS 2007: 67-74 - 2003
- [c1]Geoffrey E. Hinton, Max Welling, Andriy Mnih:
Wormholes Improve Contrastive Divergence. NIPS 2003: 417-424
Coauthor Index
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last updated on 2024-10-15 00:26 CEST by the dblp team
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