Erwin Coumans

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 36 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #35,048 of 35,345 by trust here, #27,551 by citations
• Reinforcement Learning — 62 citations into it; #2,047 of 2,242 by trust here, #992 by citations
• Control and Systems Engineering — 59 citations into it; #4,667 of 8,257 by trust here, #822 by citations
• Computer Vision and Pattern Recognition — 47 citations into it; #13,766 of 21,575 by trust here, #6,795 by citations
• Biomedical Engineering — 46 citations into it; #3,032 of 10,734 by trust here, #2,734 by citations
• Robotics & Control — 20 citations into it; #520 of 609 by trust here, #223 by citations
• Aerospace Engineering — 19 citations into it
• Optimization & Training — 12 citations into it

Whom Erwin Coumans trusts overall

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

1. Diederik P. Kingma
2. Sergey Levine
3. Xue Bin Peng
4. John Schulman
5. Geoffrey E. Hinton
6. Jan Peters
7. Alex Krizhevsky
8. Pieter Abbeel
9. Richard S. Sutton
10. Kaiming He

Who trusts Erwin Coumans overall

1. Jemin Hwangbo
2. Ashish Kumar
3. Jie Tan
4. Atil Iscen

Papers from Erwin Coumans's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Et al
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Sim-to-Real: Learning Agile Locomotion For Quadruped Robots (2018) — 981 citations
• Learning Agile Robotic Locomotion Skills by Imitating Animals (2020) — 669 citations
• Policies Modulating Trajectory Generators (2018) — 151 citations
• Optimizing Simulations with Noise-Tolerant Structured Exploration (2018) — 12 citations
• Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks (2020) — 200 citations