Kamil Saigol

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 71 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it; #20,138 of 35,345 by trust here, #15,542 by citations
• Computer Vision and Pattern Recognition — 28 citations into it
• Reinforcement Learning — 28 citations into it
• Control and Systems Engineering — 15 citations into it
• Robotics & Control — 15 citations into it
• Automotive Engineering — 14 citations into it
• Management Science and Operations Research — 13 citations into it
• Optimization & Training — 13 citations into it

Whom Kamil Saigol trusts overall

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

1. Yarin Gal
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Diederik P. Kingma
5. Radford M. Neal
6. Stéphane Ross
7. Alex Krizhevsky
8. Yann LeCun
9. Yoshua Bengio
10. Andrew Zisserman

Who trusts Kamil Saigol overall

1. Keuntaek Lee
2. Ziyi Wang

Papers from Kamil Saigol's perspective

Ordered by how strongly the researchers Kamil Saigol 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Agile Autonomous Driving using End-to-End Deep Imitation Learning (2017) — 285 citations
• Agile Off-Road Autonomous Driving Using End-to-End Deep Imitation Learning (2017) — 54 citations
• Early Failure Detection of Deep End-to-End Control Policy by Reinforcement Learning (2019) — 9 citations
• Safe end-to-end imitation learning for model predictive control (2018) — 29 citations