Andrew Delong

Computer Vision and Pattern Recognition · 2010–2018

A researcher in the Science corpus: 5 papers; trusts 138 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 72 citations into it; #9,875 of 21,575 by trust here, #12,419 by citations
• Molecular Biology — 55 citations into it; #2,366 of 17,773 by trust here, #2,641 by citations
• Artificial Intelligence — 48 citations into it; #15,857 of 35,345 by trust here, #11,072 by citations
• Generative Models — 27 citations into it
• Optimization & Training — 19 citations into it
• Computational Theory and Mathematics — 16 citations into it
• Natural Language Processing — 12 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it

Whom Andrew Delong trusts overall

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

1. Vladimir Kolmogorov
2. Yuri Boykov
3. Radford M. Neal
4. David G. Lowe
5. Geoffrey E. Hinton
6. David John Cameron MacKay
7. Uriel Feige
8. Pushmeet Kohli
9. Yoshua Bengio
10. Carsten Rother

Who trusts Andrew Delong overall

1. Liang-Chieh Chen
2. George Papandreou
3. Alan L. Yuille
4. Daniel Quang
5. Hossam N. Isack
6. Rabeeh Karimi Mahabadi
7. Kevin P. Murphy
8. Junjie Yan
9. Yuri Boykov
10. Fan Hu

Papers from Andrew Delong's perspective

Ordered by how strongly the researchers Andrew Delong 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Fast Approximate Energy Minimization with Label Costs (2010) — 603 citations
• Minimizing Energies with Hierarchical Costs (2012) — 58 citations
• Generating and designing DNA with deep generative models (2017) — 165 citations
• Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning (2015) — 2,712 citations
• Deep learning in biomedicine (2018) — 521 citations