Kumar Krishna Agrawal

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 5 papers; trusts 142 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it; #34,610 of 35,345 by trust here, #15,542 by citations
• Reinforcement Learning — 64 citations into it; #2,201 of 2,242 by trust here, #1,778 by citations
• Computer Vision and Pattern Recognition — 59 citations into it; #20,998 of 21,575 by trust here, #11,498 by citations
• Generative Models — 41 citations into it
• Optimization & Training — 20 citations into it
• Control and Systems Engineering — 17 citations into it
• Natural Language Processing — 14 citations into it
• Statistical and Nonlinear Physics — 14 citations into it

Whom Kumar Krishna Agrawal trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. John Schulman
5. Pieter Abbeel
6. Andrew Y. Ng
7. Alex Krizhevsky
8. Richard S. Sutton
9. Sergey Levine
10. Ilya Sutskever

Who trusts Kumar Krishna Agrawal overall

1. Robert Dadashi
2. Danfei Xu
3. L'eonard Hussenot
4. Misha Denil
5. Jesse Engel
6. Ilya Kostrikov
7. Lamtharn Hantrakul
8. Faraz Torabi
9. T. Paine
10. Marco Pasini

Papers from Kumar Krishna Agrawal's perspective

Ordered by how strongly the researchers Kumar Krishna Agrawal 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. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Discrete Flows: Invertible Generative Models of Discrete Data (2019) — 133 citations
• GANSynth: Adversarial Neural Audio Synthesis (2019) — 425 citations
• Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning (2018) — 296 citations
• Towards Mixed Optimization for Reinforcement Learning with Program Synthesis (2018) — 8 citations
• Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning (2018) — 111 citations