Patrick Lucey

Artificial Intelligence · 2010–2018

A researcher in the Science corpus: 5 papers; trusts 89 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #30,762 of 35,345 by trust here, #19,018 by citations
• Computer Vision and Pattern Recognition — 62 citations into it; #6,021 of 21,575 by trust here, #5,116 by citations
• Reinforcement Learning — 29 citations into it; #2,027 of 2,242 by trust here, #1,822 by citations
• Generative Models — 23 citations into it
• Experimental and Cognitive Psychology — 18 citations into it; #1,457 of 9,731 by trust here, #1,321 by citations
• Probabilistic & Bayesian Methods — 17 citations into it
• Model Architectures — 12 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Patrick Lucey trusts overall

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

1. Chih-Jen Lin
2. Iain Matthews
3. Chih-Wei Hsu
4. Diederik P. Kingma
5. Takeo Kanade
6. Chih-Wei Hsu
7. Simon Baker
8. Richard S. Sutton
9. Geoffrey E. Hinton
10. Yoshua Bengio

Who trusts Patrick Lucey overall

1. Ali Mollahosseini
2. Dimitrios Kollias
3. Tadas Baltrusaitis
4. Carlos Fabian Benitez-Quiroz
5. László A. Jeni
6. ByoungChul Ko
7. L. V. D. van der Maaten
8. Ashley D. Edwards
9. Ninad Dileep Mehendale
10. Jeffrey F. Cohn

Papers from Patrick Lucey's perspective

Ordered by how strongly the researchers Patrick Lucey 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression (2010) — 4,044 citations
• Coordinated Multi-Agent Imitation Learning (2017) — 212 citations
• Generating Multi-Agent Trajectories using Programmatic Weak Supervision (2018) — 103 citations
• Generative Multi-Agent Behavioral Cloning (2018) — 41 citations
• Painful data: The UNBC-McMaster shoulder pain expression archive database (2011) — 635 citations