Tuan Anh Le
Artificial Intelligence · 2015–2019
A researcher in the Science corpus: 6 papers; trusts 315 researchers and is trusted by 26 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 96 citations into it; #8,102 of 35,345 by trust here, #7,858 by citations
- Probabilistic & Bayesian Methods — 82 citations into it; #802 of 1,525 by trust here, #418 by citations
- Computer Vision and Pattern Recognition — 46 citations into it; #20,579 of 21,575 by trust here, #12,689 by citations
- Generative Models — 30 citations into it; #1,883 of 1,956 by trust here, #1,601 by citations
- Statistics and Probability — 24 citations into it
- Reinforcement Learning — 21 citations into it; #2,168 of 2,242 by trust here, #1,545 by citations
- Optimization & Training — 12 citations into it
- Model Architectures — 10 citations into it
Whom Tuan Anh Le trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Radford M. Neal
- Yoshua Bengio
- Ronald J. Williams
- Ruslan Salakhutdinov
- Alex Krizhevsky
- Ross B. Girshick
- Yann LeCun
- Danilo Jimenez Rezende
Who trusts Tuan Anh Le overall
- Tom Rainforth
- Atilim Gunes Baydin
- George Papamakarios
- Vaden Masrani
- Kyle Cranmer
- Frank D. Wood
- Yura N. Perov
- Willie Neiswanger
- Maximilian Igl
- Andreas Munk
Papers from Tuan Anh Le's perspective
Ordered by how strongly the researchers Tuan Anh Le trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Deep Variational Reinforcement Learning for POMDPs (2018) — 308 citations
- Tighter Variational Bounds are Not Necessarily Better (2018) — 217 citations
- Auto-Encoding Sequential Monte Carlo (2017) — 166 citations
- Inference Compilation and Universal Probabilistic Programming (2016) — 149 citations
- The Thermodynamic Variational Objective (2019) — 51 citations
- Data-driven Sequential Monte Carlo in Probabilistic Programming (2015) — 7 citations