Patrick W. Gallagher

Computer Vision and Pattern Recognition · 2014–2018

A researcher in the Science corpus: 4 papers; trusts 440 researchers and is trusted by 982 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 89 citations into it; #1,387 of 21,575 by trust here, #1,551 by citations
• Artificial Intelligence — 57 citations into it; #2,678 of 35,345 by trust here, #2,112 by citations
• Model Architectures — 40 citations into it; #845 of 2,278 by trust here, #1,014 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Optimization & Training — 13 citations into it
• Computer Networks and Communications — 6 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Media Technology — 6 citations into it

Whom Patrick W. Gallagher trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Yoshua Bengio
5. Alexey Dosovitskiy
6. Ilya Sutskever
7. Kaiming He
8. Karen Simonyan
9. Diederik P. Kingma
10. Christian Szegedy

Who trusts Patrick W. Gallagher overall

1. Chen-Yu Lee
2. Richard Zhang
3. Gao Huang
4. Lingxi Xie
5. Yoshua Bengio
6. Matthieu Courbariaux
7. Jingdong Wang
8. Zachary Chase Lipton
9. Dacheng Tao
10. Hao Chen

Papers from Patrick W. Gallagher's perspective

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

Their papers

• Deeply-Supervised Nets (2014) — 2,494 citations
• Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree (2015) — 519 citations
• What Happened to My Dog in That Network: Unraveling Top-down Generators in Convolutional Neural Networks (2015) — 6 citations
• Generalizing Pooling Functions in CNNs: Mixed, Gated, and Tree (2018) — 108 citations