← Authors

Andriy Mnih

22 papers · 2007–2024 · 5 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌍 Conference Polyglot (5) πŸƒ Academic Marathon (17) 🐝 Cross-Pollinator (14) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (18) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (3) 🌱 Topic Pioneer 🧬 Topic Evolution πŸ† Keyword Champion πŸ”₯ Unstoppable (6) πŸ’Ž Century Club (22) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (106)

Conferences

NIPS (11) ICML (7) AISTATS (2) ICLR (1) JMLR (1)

Top co-authors

George Tucker (4) Hyunjik Kim (3) Zhe Dong (2) Shakir Mohamed (2) John Lawson (2) Karol Gregor (2) Matthias Bauer (2) Chris J Maddison (2) Arnaud Doucet (2) George Papamakarios (2)

Research topics

Optimization & Theory (1)

Keywords

variational inference (11) generative model (8) gradient estimator (5) variance reduction (5) variational autoencoder (4) latent variable model (3) language modeling (3) representation learning (3) reparameterization trick (2) neural network (2) collaborative filtering (2) importance sampling (2) discrete latent variable (2) probabilistic model (2) hierarchical model (2) recommender system (2) deep learning (1) semantic representation (1) optimal transport (1) few-shot learning (1)

Papers

Schrodinger Bridge Flow for Unpaired Data Translation NIPS 2024
Compositional Score Modeling for Simulation-Based Inference ICML 2023
Generalized Doubly Reparameterized Gradient Estimators ICML 2021
Coupled Gradient Estimators for Discrete Latent Variables NIPS 2021
The Lipschitz Constant of Self-Attention ICML 2021
Sparse Orthogonal Variational Inference for Gaussian Processes AISTATS 2020
Monte Carlo Gradient Estimation in Machine Learning JMLR 2020
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables NIPS 2020
Resampled Priors for Variational Autoencoders AISTATS 2019
Attentive Neural Processes ICLR 2019
Implicit Reparameterization Gradients NIPS 2018
Disentangling by Factorising ICML 2018
Filtering Variational Objectives NIPS 2017
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models NIPS 2017
Variational Memory Addressing in Generative Models NIPS 2017
Variational Inference for Monte Carlo Objectives ICML 2016
Deep AutoRegressive Networks ICML 2014
Neural Variational Inference and Learning in Belief Networks ICML 2014
Learning word embeddings efficiently with noise-contrastive estimation NIPS 2013
Learning Label Trees for Probabilistic Modelling of Implicit Feedback NIPS 2012
A Scalable Hierarchical Distributed Language Model NIPS 2008
Probabilistic Matrix Factorization NIPS 2007