Bilal Kartal

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 202 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 171 citations into it; #21,880 of 35,345 by trust here, #18,186 by citations
• Reinforcement Learning — 166 citations into it; #1,527 of 2,242 by trust here, #1,545 by citations
• Management Science and Operations Research — 59 citations into it
• Game & Decision Theory — 37 citations into it
• Computational Theory and Mathematics — 20 citations into it
• Economics and Econometrics — 18 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Optimization & Training — 13 citations into it

Whom Bilal Kartal trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. J. Urgen Schmidhuber
4. Michael L. Littman
5. Ronald J. Williams
6. Yoshua Bengio
7. Andrew Barto
8. Diederik P. Kingma
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Bilal Kartal overall

1. Amal Feriani
2. Kaiqing Zhang
3. Hengyuan Hu
4. Andreas Holzinger

Papers from Bilal Kartal's perspective

Ordered by how strongly the researchers Bilal Kartal 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Reinforcement Learning: An Introduction
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A survey and critique of multiagent deep reinforcement learning (2018) — 732 citations