Paul Covington

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 30 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #5,428 of 35,345 by trust here, #4,276 by citations
• Information Systems — 42 citations into it; #671 of 8,771 by trust here, #341 by citations
• Management Science and Operations Research — 32 citations into it
• Recommender Systems — 25 citations into it; #42 of 233 by trust here, #36 by citations
• Reinforcement Learning — 24 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it; #4,828 of 21,575 by trust here, #4,001 by citations
• Model Architectures — 13 citations into it
• Natural Language Processing — 9 citations into it

Whom Paul Covington trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Jason Aaron Edward Weston
5. L. Eon Bottou
6. Ilya Sutskever
7. Ronald J. Williams
8. Alex Krizhevsky
9. Alex Graves
10. Ruslan Salakhutdinov

Who trusts Paul Covington overall

1. Xiangnan He
2. Guangneng Hu
3. Hongwei Wang
4. Zhe Zhao
5. Alex Beutel
6. Fajie Yuan
7. Yongfeng Zhang
8. Aminu Da’u
9. Stephen Bonner
10. Minmin Chen

Papers from Paul Covington's perspective

Ordered by how strongly the researchers Paul Covington 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. Learning Multiple Layers of Features from Tiny Images
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Deep Neural Networks for YouTube Recommendations (2016) — 3,933 citations
• Top-K Off-Policy Correction for a REINFORCE Recommender System (2018) — 558 citations
• Latent Cross: Making Use of Context in Recurrent Recommender Systems (2018) — 371 citations