Tuan Anh Le - RankMe

35 papers A* 8A 4B 4Journal 16Unranked 2

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