Michael J. Wilber

Computer Vision and Pattern Recognition · 2013–2016

A researcher in the Science corpus: 5 papers; trusts 237 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 61 citations into it; #14,251 of 21,575 by trust here, #8,612 by citations
• Artificial Intelligence — 32 citations into it; #27,209 of 35,345 by trust here, #16,771 by citations
• Model Architectures — 23 citations into it; #624 of 2,278 by trust here, #557 by citations
• Optimization & Training — 13 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Media Technology — 5 citations into it

Whom Michael J. Wilber trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Christian Szegedy
4. Kaiming He
5. David G. Lowe
6. Yoshua Bengio
7. Yann LeCun
8. Ilya Sutskever
9. Sergey Ioffe
10. Kunihiko Fukushima

Who trusts Michael J. Wilber overall

1. Seong Joon Oh
2. Sara Beery
3. Dongyoon Han
4. Daiqi Shi
5. Yoav Levine
6. Yingfan Wang
7. Boris Hanin
8. Maximilian Pichler
9. Andreas Veit
10. Zifeng Wu

Papers from Michael J. Wilber's perspective

Ordered by how strongly the researchers Michael J. Wilber 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Residual Networks are Exponential Ensembles of Relatively Shallow Networks (2016) — 109 citations
• Can we still avoid automatic face detection? (2016) — 58 citations
• Learning Concept Embeddings with Combined Human-Machine Expertise (2015) — 40 citations
• Residual Networks Behave Like Ensembles of Relatively Shallow Networks (2016) — 1,142 citations
• Animal recognition in the Mojave Desert: Vision tools for field biologists (2013) — 46 citations