Get our free extension to see links to code for papers anywhere online!

Add to Chrome

Add to Firefox

Picture for Andriy Mnih

Andriy Mnih

Score Modeling for Simulation-based Inference

Sep 28, 2022
Tomas Geffner, George Papamakarios, Andriy Mnih

Figure 1 for Score Modeling for Simulation-based Inference

Figure 2 for Score Modeling for Simulation-based Inference

Access Paper or Ask Questions

Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts

Sep 24, 2021
Wouter Kool, Chris J. Maddison, Andriy Mnih

Figure 1 for Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts

Figure 2 for Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts

Figure 3 for Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts

Access Paper or Ask Questions

Coupled Gradient Estimators for Discrete Latent Variables

Jun 15, 2021
Zhe Dong, Andriy Mnih, George Tucker

Figure 1 for Coupled Gradient Estimators for Discrete Latent Variables

Figure 2 for Coupled Gradient Estimators for Discrete Latent Variables

Figure 3 for Coupled Gradient Estimators for Discrete Latent Variables

Figure 4 for Coupled Gradient Estimators for Discrete Latent Variables

* Under Review

Access Paper or Ask Questions

Generalized Doubly Reparameterized Gradient Estimators

Jan 26, 2021
Matthias Bauer, Andriy Mnih

Figure 1 for Generalized Doubly Reparameterized Gradient Estimators

Figure 2 for Generalized Doubly Reparameterized Gradient Estimators

Figure 3 for Generalized Doubly Reparameterized Gradient Estimators

Figure 4 for Generalized Doubly Reparameterized Gradient Estimators

Access Paper or Ask Questions

DisARM: An Antithetic Gradient Estimator for Binary Latent Variables

Jun 18, 2020
Zhe Dong, Andriy Mnih, George Tucker

Figure 1 for DisARM: An Antithetic Gradient Estimator for Binary Latent Variables

Figure 2 for DisARM: An Antithetic Gradient Estimator for Binary Latent Variables

Figure 3 for DisARM: An Antithetic Gradient Estimator for Binary Latent Variables

Figure 4 for DisARM: An Antithetic Gradient Estimator for Binary Latent Variables

Access Paper or Ask Questions

The Lipschitz Constant of Self-Attention

Jun 08, 2020
Hyunjik Kim, George Papamakarios, Andriy Mnih

Figure 1 for The Lipschitz Constant of Self-Attention

Figure 2 for The Lipschitz Constant of Self-Attention

Figure 3 for The Lipschitz Constant of Self-Attention

Figure 4 for The Lipschitz Constant of Self-Attention

Access Paper or Ask Questions

Q-Learning in enormous action spaces via amortized approximate maximization

Jan 22, 2020
Tom Van de Wiele, David Warde-Farley, Andriy Mnih, Volodymyr Mnih

Figure 1 for Q-Learning in enormous action spaces via amortized approximate maximization

Figure 2 for Q-Learning in enormous action spaces via amortized approximate maximization

Figure 3 for Q-Learning in enormous action spaces via amortized approximate maximization

Figure 4 for Q-Learning in enormous action spaces via amortized approximate maximization

* A previous version of this work appeared at the Deep Reinforcement Learning Workshop, NeurIPS 2018

Access Paper or Ask Questions

Sparse Orthogonal Variational Inference for Gaussian Processes

Oct 24, 2019
Jiaxin Shi, Michalis K. Titsias, Andriy Mnih

Figure 1 for Sparse Orthogonal Variational Inference for Gaussian Processes

Figure 2 for Sparse Orthogonal Variational Inference for Gaussian Processes

Figure 3 for Sparse Orthogonal Variational Inference for Gaussian Processes

Figure 4 for Sparse Orthogonal Variational Inference for Gaussian Processes

Access Paper or Ask Questions

Monte Carlo Gradient Estimation in Machine Learning

Jun 25, 2019
Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih

Figure 1 for Monte Carlo Gradient Estimation in Machine Learning

Figure 2 for Monte Carlo Gradient Estimation in Machine Learning

Figure 3 for Monte Carlo Gradient Estimation in Machine Learning

Figure 4 for Monte Carlo Gradient Estimation in Machine Learning

* 59 pages, under review

Access Paper or Ask Questions