Tambet Matiisen

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 3 papers; trusts 137 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it; #17,453 of 35,345 by trust here, #6,664 by citations
• Computer Vision and Pattern Recognition — 79 citations into it; #18,842 of 21,575 by trust here, #8,831 by citations
• Reinforcement Learning — 66 citations into it; #1,522 of 2,242 by trust here, #926 by citations
• Automotive Engineering — 45 citations into it
• Cognitive Neuroscience — 13 citations into it
• Management Science and Operations Research — 11 citations into it
• Model Architectures — 11 citations into it
• Optimization & Training — 8 citations into it

Whom Tambet Matiisen trusts overall

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

1. Richard S. Sutton
2. Ilya Sutskever
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. J. Urgen Schmidhuber
6. Yoshua Bengio
7. Alex Krizhevsky
8. Kaiming He
9. Alex Graves
10. Ronald J. Williams

Who trusts Tambet Matiisen overall

1. Jonathon Byrd
2. Tabish Rashid
3. Sanmit Narvekar
4. Xin Wang
5. Benjamin Eysenbach
6. Jakob N. Foerster
7. Petru Soviany
8. Anirudh Srinivasan
9. Gabriel B. Margolis
10. Alex Peysakhovich

Papers from Tambet Matiisen's perspective

Ordered by how strongly the researchers Tambet Matiisen 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Teacher–Student Curriculum Learning (2017) — 471 citations
• Multiagent cooperation and competition with deep reinforcement learning (2015) — 1,014 citations
• A Survey of End-to-End Driving: Architectures and Training Methods (2020) — 323 citations