Jayesh K. Gupta

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 5 papers; trusts 105 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it; #5,138 of 35,345 by trust here, #7,606 by citations
• Reinforcement Learning — 66 citations into it; #528 of 2,242 by trust here, #778 by citations
• Computer Vision and Pattern Recognition — 35 citations into it
• Model Architectures — 15 citations into it
• Optimization & Training — 14 citations into it
• Management Science and Operations Research — 13 citations into it; #5,352 of 11,946 by trust here, #4,430 by citations
• Control and Systems Engineering — 11 citations into it
• Computational Theory and Mathematics — 8 citations into it; #1,623 of 12,182 by trust here, #1,782 by citations

Whom Jayesh K. Gupta trusts overall

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

1. John Schulman
2. Sergey Levine
3. Pieter Abbeel
4. Richard S. Sutton
5. Andrew Y. Ng
6. Geoffrey E. Hinton
7. Ronald J. Williams
8. Yoshua Bengio
9. Diederik P. Kingma
10. Volodymyr Mnih

Who trusts Jayesh K. Gupta overall

1. Maximilian Hüttenrauch
2. Gregory Palmer
3. Pablo Hernandez-Leal
4. Jakob N. Foerster
5. Filippos Christianos
6. Shariq Iqbal
7. Kaiqing Zhang
8. Sanjeevan Ahilan
9. Yasar Sinan Nasir
10. Kun Shao

Papers from Jayesh K. Gupta's perspective

Ordered by how strongly the researchers Jayesh K. Gupta 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Cooperative Multi-agent Control Using Deep Reinforcement Learning (2017) — 1,047 citations
• Model-Free Imitation Learning with Policy Optimization (2016) — 157 citations
• Learning Policy Representations in Multiagent Systems (2018) — 137 citations
• ClimaX: A foundation model for weather and climate (2023) — 473 citations
• Model Primitive Hierarchical Lifelong Reinforcement Learning (2019) — 10 citations