Sanmit Narvekar

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 63 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it; #31,627 of 35,345 by trust here, #16,471 by citations
• Reinforcement Learning — 82 citations into it
• Management Science and Operations Research — 21 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Information Systems — 14 citations into it
• Meta & Continual Learning — 14 citations into it
• Computational Theory and Mathematics — 13 citations into it
• Control and Systems Engineering — 11 citations into it; #7,514 of 8,257 by trust here, #5,784 by citations

Whom Sanmit Narvekar trusts overall

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

1. Richard S. Sutton
2. Matthew E. Taylor
3. Andrew Barto
4. Peter Stone
5. Yoshua Bengio
6. Geoffrey E. Hinton
7. J. Urgen Schmidhuber
8. Alessandro Lazaric
9. George Dimitri Konidaris
10. Alex Graves

Who trusts Sanmit Narvekar overall

1. Weixun Wang
2. Peter Stone
3. Pascal Klink
4. Xin Wang
5. Tianpei Yang
6. Bei Peng
7. Carlo D'Eramo
8. Y. Liu
9. Matteo Leonetti
10. Yudong Chen

Papers from Sanmit Narvekar's perspective

Ordered by how strongly the researchers Sanmit Narvekar 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Reinforcement Learning: An Introduction
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning (2017) — 123 citations
• Learning Curriculum Policies for Reinforcement Learning (2018) — 100 citations
• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey (2020) — 748 citations
• RecSim: A Configurable Simulation Platform for Recommender Systems (2019) — 215 citations
• Source Task Creation for Curriculum Learning (2016) — 130 citations