Mohit Sewak

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 20 researchers and is trusted by 189 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it; #18,838 of 35,345 by trust here, #34,213 by citations
• Reinforcement Learning — 23 citations into it; #134 of 2,242 by trust here, #614 by citations
• Control and Systems Engineering — 7 citations into it
• Management Science and Operations Research — 5 citations into it; #89 of 11,946 by trust here, #720 by citations
• Mathematical Optimization — 5 citations into it

Whom Mohit Sewak trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Csaba Szepesvari
4. Diederik P. Kingma
5. J. Urgen Schmidhuber
6. Alex Krizhevsky
7. Ronald J. Williams
8. Yann LeCun
9. Yoshua Bengio
10. Dimitri P. Bertsekas

Who trusts Mohit Sewak overall

1. Xue Bin Peng
2. Sergey Levine
3. Ofir Nachum
4. Ahmed Touati
5. Martha White
6. William T. B. Uther
7. Jiachen Yang
8. Pieter Abbeel
9. Aniruddh Raghu
10. Todd Hester

Papers from Mohit Sewak's perspective

Ordered by how strongly the researchers Mohit Sewak 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Introduction to Reinforcement Learning (2020) — 1,879 citations
• Introduction to Deep Learning (2019) — 316 citations