Tuan Anh Le

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 32

Top Topics

Sequential Monte Carlo

Generative Model

Sufficient Statistics

Top Venues

Publications

Kunal Jha, Tuan Anh Le, Chuanyang Jin, Yen-Ling Kuo, Joshua B. Tenenbaum, Tianmin Shu
Neural Amortized Inference for Nested Multi-Agent Reasoning. AAAI (2024)
Kunal Jha, Tuan Anh Le, Chuanyang Jin, Yen-Ling Kuo, Joshua B. Tenenbaum, Tianmin Shu
Neural Amortized Inference for Nested Multi-agent Reasoning. CoRR (2023)
Yoni Friedman, Thomas P. O'Connell, Max H. Siegel, Daniel M. Bear, Tuan Anh Le, Bernhard Egger, Josh Tenenbaum
Benchmarking mid-level vision with texture-defined 3D objects. CogSci (2022)
Yichao Liang, Josh Tenenbaum, Tuan Anh Le, N. Siddharth
Drawing out of Distribution with Neuro-Symbolic Generative Models. NeurIPS (2022)
Tuan Anh Le, Katherine M. Collins, Luke Hewitt, Kevin Ellis, Siddharth Narayanaswamy, Samuel Gershman, Joshua B. Tenenbaum
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface. ICLR (2022)
Yichao Liang, Joshua B. Tenenbaum, Tuan Anh Le, N. Siddharth
Drawing out of Distribution with Neuro-Symbolic Generative Models. CoRR (2022)
Matthias Hofer, Tuan Anh Le, Roger Levy, Joshua B. Tenenbaum
Learning Evolved Combinatorial Symbols with a Neuro-symbolic Generative Model. CoRR (2021)
Tuan Anh Le, Katherine M. Collins, Luke Hewitt, Kevin Ellis, N. Siddharth, Samuel J. Gershman, Joshua B. Tenenbaum
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface. CoRR (2021)
Matthias Hofer, Tuan Anh Le, Roger Levy, Josh Tenenbaum
Learning Evolved Combinatorial Symbols with a Neuro-symbolic Generative Model. CogSci (2021)
Yoni Friedman, Tuan Anh Le, Bernhard Egger, Max H. Siegel, Josh Tenenbaum
Explaining the Gestalt principle of common fate as amortized inference. CogSci (2021)
Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood
Semi-supervised Sequential Generative Models. UAI (2020)
Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de Meent
Amortized Population Gibbs Samplers with Neural Sufficient Statistics. ICML (2020)
Luke B. Hewitt, Tuan Anh Le, Joshua B. Tenenbaum
Learning to learn generative programs with Memoised Wake-Sleep. CoRR (2020)
Luke B. Hewitt, Tuan Anh Le, Joshua B. Tenenbaum
Learning to learn generative programs with Memoised Wake-Sleep. UAI (2020)
Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood
Semi-supervised Sequential Generative Models. CoRR (2020)
Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow. UAI (2019)
Vaden Masrani, Tuan Anh Le, Frank Wood
The Thermodynamic Variational Objective. CoRR (2019)
Vaden Masrani, Tuan Anh Le, Frank Wood
The Thermodynamic Variational Objective. NeurIPS (2019)
Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de Meent
Amortized Population Gibbs Samplers with Neural Sufficient Statistics. CoRR (2019)
Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood
Revisiting Reweighted Wake-Sleep. CoRR (2018)
Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood
Auto-Encoding Sequential Monte Carlo. ICLR (Poster) (2018)
Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh
Tighter Variational Bounds are Not Necessarily Better. CoRR (2018)
Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson
Deep Variational Reinforcement Learning for POMDPs. ICML (2018)
Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh
Tighter Variational Bounds are Not Necessarily Better. ICML (2018)
Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson
Deep Variational Reinforcement Learning for POMDPs. CoRR (2018)
Mario Lezcano Casado, Atilim Gunes Baydin, David Martínez-Rubio, Tuan Anh Le, Frank D. Wood, Lukas Alexander Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. CoRR (2017)
Tuan Anh Le, Atilim Günes Baydin, Robert Zinkov, Frank D. Wood
Using synthetic data to train neural networks is model-based reasoning. IJCNN (2017)
Tuan Anh Le, Atilim Gunes Baydin, Frank D. Wood
Inference Compilation and Universal Probabilistic Programming. AISTATS (2017)
Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov, Frank D. Wood
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning. CoRR (2017)
Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood
Bayesian Optimization for Probabilistic Programs. CoRR (2017)
Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood
Bayesian Optimization for Probabilistic Programs. NIPS (2016)
Tuan Anh Le, Atilim Gunes Baydin, Frank D. Wood
Inference Compilation and Universal Probabilistic Programming. CoRR (2016)
Yura N. Perov, Tuan Anh Le, Frank D. Wood
Data-driven Sequential Monte Carlo in Probabilistic Programming. CoRR (2015)