Afshin Oroojlooyjadid

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 233 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #21,630 of 35,345 by trust here, #27,551 by citations
• Reinforcement Learning — 88 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it
• Computer Networks and Communications — 22 citations into it
• Computational Theory and Mathematics — 21 citations into it; #7,279 of 12,182 by trust here, #7,851 by citations
• Control and Systems Engineering — 18 citations into it
• Management Science and Operations Research — 15 citations into it
• Electrical and Electronic Engineering — 10 citations into it

Whom Afshin Oroojlooyjadid trusts overall

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

1. Richard S. Sutton
2. Ronald J. Williams
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. David Silver
6. Diederik P. Kingma
7. Hado van Hasselt
8. Alex Krizhevsky
9. Karen Simonyan
10. Volodymyr Mnih

Who trusts Afshin Oroojlooyjadid overall

1. Jieyi Long
2. Yu Bai
3. Sven Gronauer
4. Chi Jin
5. Wendelin Böhmer

Papers from Afshin Oroojlooyjadid's perspective

Ordered by how strongly the researchers Afshin Oroojlooyjadid 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

• A review of cooperative multi-agent deep reinforcement learning (2019) — 635 citations