Harish Chaandar Ravichandar

Control and Systems Engineering · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 98 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 87 citations into it; #6,026 of 8,257 by trust here, #4,722 by citations
• Artificial Intelligence — 78 citations into it; #26,533 of 35,345 by trust here, #25,040 by citations
• Reinforcement Learning — 53 citations into it
• Computer Vision and Pattern Recognition — 37 citations into it
• Robotics & Control — 26 citations into it
• Biomedical Engineering — 22 citations into it
• Cognitive Neuroscience — 15 citations into it
• Aerospace Engineering — 11 citations into it

Whom Harish Chaandar Ravichandar trusts overall

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

1. Seyed Mohammad Khansari-Zadeh
2. Stefan Schaal
3. Aude Gemma Billard
4. Pieter Abbeel
5. Klaus Neumann
6. Sergey Levine
7. Andrew Y. Ng
8. J. Bagnell
9. Peter Pastor
10. Chelsea Finn

Who trusts Harish Chaandar Ravichandar overall

1. Yuke Zhu
2. Athanasios S. Polydoros
3. Sonia Chernova
4. Josiah Wong
5. Muhammad Asif Rana

Papers from Harish Chaandar Ravichandar's perspective

Ordered by how strongly the researchers Harish Chaandar Ravichandar 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Recent Advances in Robot Learning from Demonstration (2020) — 877 citations
• Learning position and orientation dynamics from demonstrations via contraction analysis (2018) — 45 citations
• Learning Partially Contracting Dynamical Systems from Demonstrations (2017) — 67 citations