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Tuan Anh Le 0001
Person information
- affiliation: Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, Cambridge, MA, USA
- affiliation (PhD 2020): University of Oxford, UK
Other persons with the same name
- Tuan Anh Le (aka: Tuan-Anh Le) — disambiguation page
- Tuan Anh Le 0002
— Middlesex University, Faculty of Science and Technology, London, UK (and 1 more)
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2020 – today
- 2024
- [c17]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: 530-537 - 2023
- [i16]Kunal Jha, Tuan Anh Le, Chuanyang Jin, Yen-Ling Kuo, Joshua B. Tenenbaum, Tianmin Shu:
Neural Amortized Inference for Nested Multi-agent Reasoning. CoRR abs/2308.11071 (2023) - 2022
- [c16]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 - [c15]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 - [c14]Yichao Liang, Josh Tenenbaum, Tuan Anh Le, N. Siddharth:
Drawing out of Distribution with Neuro-Symbolic Generative Models. NeurIPS 2022 - [i15]Yichao Liang, Joshua B. Tenenbaum, Tuan Anh Le, N. Siddharth:
Drawing out of Distribution with Neuro-Symbolic Generative Models. CoRR abs/2206.01829 (2022) - 2021
- [c13]Matthias Hofer, Tuan Anh Le, Roger Levy, Josh Tenenbaum:
Learning Evolved Combinatorial Symbols with a Neuro-symbolic Generative Model. CogSci 2021 - [c12]Yoni Friedman, Tuan Anh Le, Bernhard Egger, Max H. Siegel, Josh Tenenbaum:
Explaining the Gestalt principle of common fate as amortized inference. CogSci 2021 - [i14]Matthias Hofer, Tuan Anh Le, Roger Levy, Joshua B. Tenenbaum:
Learning Evolved Combinatorial Symbols with a Neuro-symbolic Generative Model. CoRR abs/2104.08274 (2021) - [i13]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 abs/2107.06393 (2021) - 2020
- [b1]Tuan Anh Le:
Amortized inference and model learning for probabilistic programming. University of Oxford, UK, 2020 - [c11]Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de Meent:
Amortized Population Gibbs Samplers with Neural Sufficient Statistics. ICML 2020: 10421-10431 - [c10]Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood:
Semi-supervised Sequential Generative Models. UAI 2020: 649-658 - [c9]Luke B. Hewitt, Tuan Anh Le, Joshua B. Tenenbaum:
Learning to learn generative programs with Memoised Wake-Sleep. UAI 2020: 1278-1287 - [i12]Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood:
Semi-supervised Sequential Generative Models. CoRR abs/2007.00155 (2020) - [i11]Luke B. Hewitt, Tuan Anh Le, Joshua B. Tenenbaum:
Learning to learn generative programs with Memoised Wake-Sleep. CoRR abs/2007.03132 (2020)
2010 – 2019
- 2019
- [c8]Vaden Masrani, Tuan Anh Le, Frank Wood:
The Thermodynamic Variational Objective. NeurIPS 2019: 11521-11530 - [c7]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: 1039-1049 - [i10]Vaden Masrani, Tuan Anh Le, Frank Wood:
The Thermodynamic Variational Objective. CoRR abs/1907.00031 (2019) - [i9]Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de Meent:
Amortized Population Gibbs Samplers with Neural Sufficient Statistics. CoRR abs/1911.01382 (2019) - 2018
- [c6]Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood:
Auto-Encoding Sequential Monte Carlo. ICLR (Poster) 2018 - [c5]Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson:
Deep Variational Reinforcement Learning for POMDPs. ICML 2018: 2122-2131 - [c4]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: 4274-4282 - [i8]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 abs/1802.04537 (2018) - [i7]Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood:
Revisiting Reweighted Wake-Sleep. CoRR abs/1805.10469 (2018) - [i6]Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson:
Deep Variational Reinforcement Learning for POMDPs. CoRR abs/1806.02426 (2018) - 2017
- [c3]Tuan Anh Le, Atilim Gunes Baydin, Frank D. Wood:
Inference Compilation and Universal Probabilistic Programming. AISTATS 2017: 1338-1348 - [c2]Tuan Anh Le, Atilim Günes Baydin
Using synthetic data to train neural networks is model-based reasoning. IJCNN 2017: 3514-3521 - [i5]Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov, Frank D. Wood:
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning. CoRR abs/1703.00868 (2017) - [i4]Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood:
Bayesian Optimization for Probabilistic Programs. CoRR abs/1707.04314 (2017) - [i3]Mario Lezcano Casado, Atilim Gunes Baydin, David Martínez-Rubio, Tuan Anh Le, Frank D. Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat:
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. CoRR abs/1712.07901 (2017) - 2016
- [c1]Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood:
Bayesian Optimization for Probabilistic Programs. NIPS 2016: 280-288 - [i2]Tuan Anh Le, Atilim Gunes Baydin, Frank D. Wood:
Inference Compilation and Universal Probabilistic Programming. CoRR abs/1610.09900 (2016) - 2015
- [i1]Yura N. Perov, Tuan Anh Le, Frank D. Wood:
Data-driven Sequential Monte Carlo in Probabilistic Programming. CoRR abs/1512.04387 (2015)
Coauthor Index
aka: Frank Wood
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