Michele Covell

Computer Vision and Pattern Recognition · 2015–2017

A researcher in the Science corpus: 4 papers; trusts 42 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 37 citations into it; #7,086 of 21,575 by trust here, #4,690 by citations
• Artificial Intelligence — 28 citations into it; #25,838 of 35,345 by trust here, #31,361 by citations
• Model Architectures — 16 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Generative Models — 6 citations into it; #1,112 of 1,956 by trust here, #989 by citations
• Media Technology — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Michele Covell trusts overall

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

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

Who trusts Michele Covell overall

1. Shumeet Baluja
2. Eirikur Agustsson
3. David C. Minnen
4. Ian S. Fischer
5. Fabian Mentzer
6. Guo Lu
7. Michael Tschannen

Papers from Michele Covell's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. ImageNet Large Scale Visual Recognition Challenge
6. Gradient-based learning applied to document recognition
7. Going deeper with convolutions
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Full Resolution Image Compression with Recurrent Neural Networks (2016) — 893 citations
• Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks (2017) — 401 citations
• Variable Rate Image Compression with Recurrent Neural Networks (2015) — 829 citations
• The Virtues of Peer Pressure: A Simple Method for Discovering High-Value Mistakes (2015) — 9 citations