Rishabh Agarwal

Artificial Intelligence · with Norouzi · 2019–2021

A researcher in the Science corpus: 9 papers; trusts 370 researchers and is trusted by 65 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 312 citations into it; #9,494 of 35,345 by trust here, #6,956 by citations
• Reinforcement Learning — 272 citations into it; #321 of 2,242 by trust here, #523 by citations
• Computer Vision and Pattern Recognition — 81 citations into it
• Management Science and Operations Research — 68 citations into it
• Evaluation & Benchmarks — 53 citations into it
• Control and Systems Engineering — 41 citations into it
• Computational Theory and Mathematics — 36 citations into it; #3,332 of 12,182 by trust here, #1,851 by citations
• Optimization & Training — 31 citations into it

Whom Rishabh Agarwal trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Marc G. Bellemare
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Yoshua Bengio
7. Yann LeCun
8. Ronald J. Williams
9. Volodymyr Mnih
10. Ilya Sutskever

Who trusts Rishabh Agarwal overall

1. Sergey Levine
2. Ofir Nachum
3. Aviral Kumar
4. Ruosong Wang
5. Caglar Gulcehre
6. Wenhu Chen
7. Tianhe Yu
8. George Tucker
9. Rasool Fakoor
10. Ziyun Wang

Papers from Rishabh Agarwal's perspective

Ordered by how strongly the researchers Rishabh Agarwal 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. 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

• Striving for Simplicity in Off-policy Deep Reinforcement Learning (2019) — 71 citations
• RL Unplugged: Benchmarks for Offline Reinforcement Learning (2020) — 74 citations
• Deep Reinforcement Learning at the Edge of the Statistical Precipice (2021) — 950 citations
• Learning to Generalize from Sparse and Underspecified Rewards (2019) — 105 citations
• Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning (2021) — 197 citations
• RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning (2020)
• An Optimistic Perspective on Offline Deep Reinforcement Learning (2020) — 22 citations
• Neural Additive Models: Interpretable Machine Learning with Neural Nets (2020) — 612 citations
• An Optimistic Perspective on Offline Reinforcement Learning (2019) — 581 citations