Craig Ferguson

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 63 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 125 citations into it; #14,813 of 35,345 by trust here, #7,310 by citations
• Natural Language Processing — 81 citations into it; #2,820 of 5,688 by trust here, #1,674 by citations
• Reinforcement Learning — 79 citations into it; #1,121 of 2,242 by trust here, #681 by citations
• Computer Vision and Pattern Recognition — 21 citations into it; #10,519 of 21,575 by trust here, #5,806 by citations
• Social Psychology — 18 citations into it
• Computational Theory and Mathematics — 13 citations into it
• Experimental and Cognitive Psychology — 12 citations into it
• Language and Linguistics — 8 citations into it

Whom Craig Ferguson trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Iulian Serban
5. Richard S. Sutton
6. Alex Krizhevsky
7. Oriol Vinyals
8. Ilya Sutskever
9. Yann LeCun
10. Alex Graves

Who trusts Craig Ferguson overall

1. Aviral Kumar
2. Sergey Levine
3. Natasha Jaques
4. Rafael Rafailov
5. Tomasz Korbak
6. Scott Fujimoto
7. Tianhe Yu

Papers from Craig Ferguson's perspective

Ordered by how strongly the researchers Craig Ferguson trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog (2019) — 397 citations
• Human-centric Dialog Training via Offline Reinforcement Learning (2020) — 120 citations
• Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems (2019) — 100 citations