Shikhar Bahl

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 5 papers; trusts 202 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 167 citations into it; #8,496 of 35,345 by trust here, #5,749 by citations
• Computer Vision and Pattern Recognition — 127 citations into it
• Reinforcement Learning — 120 citations into it; #1,015 of 2,242 by trust here, #885 by citations
• Control and Systems Engineering — 80 citations into it; #2,182 of 8,257 by trust here, #413 by citations
• Robotics & Control — 38 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Computational Theory and Mathematics — 18 citations into it
• Model Architectures — 11 citations into it

Whom Shikhar Bahl trusts overall

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

1. Kaiming He
2. Sergey Levine
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Yoshua Bengio
7. Richard S. Sutton
8. Yann LeCun
9. Ross B. Girshick
10. J. Urgen Schmidhuber

Who trusts Shikhar Bahl overall

1. Suraj Nair
2. Benjamin Eysenbach
3. Sergey Levine
4. Corey Lynch
5. Dibya Ghosh
6. Francesco Locatello
7. Chelsea Finn
8. Charles B. Schaff
9. Tianhe Yu

Papers from Shikhar Bahl's perspective

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

Their papers

• Visual Reinforcement Learning with Imagined Goals (2018) — 603 citations
• Skew-Fit: State-Covering Self-Supervised Reinforcement Learning (2019) — 313 citations
• Residual Reinforcement Learning for Robot Control (2018) — 624 citations
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 (2023) — 1,105 citations
• Affordances from Human Videos as a Versatile Representation for Robotics (2023) — 337 citations