Thanard Kurutach

Reinforcement Learning · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 219 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 120 citations into it; #897 of 2,242 by trust here, #716 by citations
• Artificial Intelligence — 110 citations into it; #12,688 of 35,345 by trust here, #10,430 by citations
• Computer Vision and Pattern Recognition — 54 citations into it; #18,616 of 21,575 by trust here, #9,187 by citations
• Control and Systems Engineering — 42 citations into it
• Robotics & Control — 20 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Biomedical Engineering — 17 citations into it
• Generative Models — 14 citations into it; #1,745 of 1,956 by trust here, #1,221 by citations

Whom Thanard Kurutach trusts overall

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

1. Sergey Levine
2. Diederik P. Kingma
3. Richard S. Sutton
4. Pieter Abbeel
5. Chelsea Finn
6. Geoffrey E. Hinton
7. John Schulman
8. Yoshua Bengio
9. Alex Krizhevsky
10. Kaiming He

Who trusts Thanard Kurutach overall

1. Sergey Levine
2. Ignasi Clavera
3. Pieter Abbeel
4. Danijar Hafner
5. Tingwu Wang
6. Anusha Nagabandi
7. Jimmy Ba
8. Chelsea Finn
9. Kevin Lu
10. Danfei Xu

Papers from Thanard Kurutach's perspective

Ordered by how strongly the researchers Thanard Kurutach 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Model-Ensemble Trust-Region Policy Optimization (2018) — 487 citations
• Learning Plannable Representations with Causal InfoGAN (2018) — 199 citations
• Mastering Atari Games with Limited Data (2021) — 335 citations
• Learning to Manipulate Deformable Objects without Demonstrations (2019) — 231 citations