Avi Singh

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 9 papers; trusts 601 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 301 citations into it; #4,520 of 35,345 by trust here, #3,023 by citations
• Reinforcement Learning — 219 citations into it; #1,632 of 2,242 by trust here, #1,331 by citations
• Computer Vision and Pattern Recognition — 155 citations into it; #7,388 of 21,575 by trust here, #3,613 by citations
• Control and Systems Engineering — 131 citations into it; #1,274 of 8,257 by trust here, #515 by citations
• Robotics & Control — 58 citations into it; #353 of 609 by trust here, #384 by citations
• Natural Language Processing — 37 citations into it; #2,923 of 5,688 by trust here, #1,150 by citations
• Biomedical Engineering — 33 citations into it
• Cognitive Neuroscience — 23 citations into it

Whom Avi Singh trusts overall

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

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

Who trusts Avi Singh overall

1. Chelsea Finn
2. Sergey Levine
3. Benjamin Eysenbach
4. Laura M. Smith
5. Tianhe Yu
6. Aravind Rajeswaran
7. Corey Lynch
8. Suraj Nair
9. Abhishek Gupta
10. Brian Ichter

Papers from Avi Singh's perspective

Ordered by how strongly the researchers Avi Singh 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Visual Dialog (2016) — 1,102 citations
• Divide-and-Conquer Reinforcement Learning (2017) — 135 citations
• End-to-End Robotic Reinforcement Learning without Reward Engineering (2019) — 289 citations
• Few-Shot Goal Inference for Visuomotor Learning and Planning (2018) — 72 citations
• Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition (2018) — 133 citations
• The Ingredients of Real-World Robotic Reinforcement Learning (2020) — 207 citations
• Parrot: Data-Driven Behavioral Priors for Reinforcement Learning (2020) — 172 citations
• GPLAC: Generalizing Vision-Based Robotic Skills Using Weakly Labeled Images (2017) — 23 citations
• COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning (2020) — 116 citations