Saurabh Kumar

Artificial Intelligence · with Bellemare · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 155 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 133 citations into it; #15,337 of 35,345 by trust here, #7,858 by citations
• Reinforcement Learning — 112 citations into it; #840 of 2,242 by trust here, #1,139 by citations
• Computer Vision and Pattern Recognition — 39 citations into it
• Management Science and Operations Research — 17 citations into it
• Representation & Self-Supervised Learning — 16 citations into it; #2,427 of 3,145 by trust here, #2,066 by citations
• Computational Theory and Mathematics — 14 citations into it
• Control and Systems Engineering — 13 citations into it
• Electrical and Electronic Engineering — 11 citations into it

Whom Saurabh Kumar trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Marc G. Bellemare
6. Alex Krizhevsky
7. Yann LeCun
8. Andrew Barto
9. Ilya Sutskever
10. Kaiming He

Who trusts Saurabh Kumar overall

1. Pablo Samuel Castro
2. Sanjeevan Ahilan
3. Ofir Nachum
4. Amy Zhang
5. Michael Crawshaw
6. Aviral Kumar
7. Christopher Fifty
8. Rishabh Agarwal
9. Sergey Levine
10. Mengjiao Yang

Papers from Saurabh Kumar's perspective

Ordered by how strongly the researchers Saurabh Kumar 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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• DeepMDP: Learning Continuous Latent Space Models for Representation Learning (2019) — 351 citations
• Gradient Surgery for Multi-Task Learning (2020) — 1,977 citations
• Learning to Compose Skills (2017) — 44 citations
• Dopamine: A Research Framework for Deep Reinforcement Learning (2018) — 296 citations
• Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning (2017) — 36 citations