Nat Dilokthanakul

Reinforcement Learning · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 38 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 50 citations into it
• Artificial Intelligence — 48 citations into it; #26,736 of 35,345 by trust here, #12,387 by citations
• Computer Vision and Pattern Recognition — 23 citations into it; #16,409 of 21,575 by trust here, #9,563 by citations
• Generative Models — 15 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Nat Dilokthanakul trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Richard S. Sutton
4. Alex Graves
5. Yoshua Bengio
6. Andrew Barto
7. Yann LeCun
8. J. Urgen Schmidhuber
9. Jimmy Ba
10. Volodymyr Mnih

Who trusts Nat Dilokthanakul overall

1. Fei Ye
2. J. Tomczak
3. Ye Yuan
4. Matthias Bauer
5. Shuyu Lin
6. Adrian G. Bors
7. Peter Sorrenson
8. Andriy Mnih
9. Benoit Gaujac
10. Lyle Regenwetter

Papers from Nat Dilokthanakul's perspective

Ordered by how strongly the researchers Nat Dilokthanakul trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders (2016) — 734 citations
• Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning (2017) — 103 citations
• Classifying Options for Deep Reinforcement Learning (2016) — 22 citations