Anurag Koul

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 41 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #24,990 of 35,345 by trust here, #16,471 by citations
• Reinforcement Learning — 18 citations into it; #1,493 of 2,242 by trust here, #1,280 by citations
• Computer Vision and Pattern Recognition — 14 citations into it
• Safety, Robustness & Interpretability — 12 citations into it
• Model Architectures — 9 citations into it

Whom Anurag Koul trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Volodymyr Mnih
5. Alex Krizhevsky
6. Karen Simonyan
7. Jimmy Ba
8. Alex Graves
9. Yann LeCun
10. Ilya Sutskever

Who trusts Anurag Koul overall

1. Ramitha Sundar
2. Amy Zhang
3. Raghuram Mandyam Annasamy
4. Alexandre Heuillet
5. Akanksha Atrey
6. Wojciech Samek
7. Christian Rupprecht
8. Dmitry Nikulin
9. Daniel S. Brown
10. Zhao Yang

Papers from Anurag Koul's perspective

Ordered by how strongly the researchers Anurag Koul trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Visualizing and Understanding Atari Agents (2017) — 406 citations
• Learning Finite State Representations of Recurrent Policy Networks (2018) — 99 citations
• Explainable Reinforcement Learning via Reward Decomposition (2019)