Kevin L. Briggman

Computer Vision and Pattern Recognition · 2007–2013

A researcher in the Science corpus: 7 papers; trusts 81 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 50 citations into it; #14,458 of 21,575 by trust here, #7,249 by citations
• Neuroscience — 14 citations into it
• Structural Biology — 14 citations into it; #54 of 224 by trust here, #84 by citations
• Molecular Biology — 11 citations into it; #6,818 of 17,773 by trust here, #6,338 by citations
• Surfaces, Coatings and Films — 11 citations into it
• Cellular and Molecular Neuroscience — 9 citations into it; #1,114 of 2,998 by trust here, #789 by citations

Whom Kevin L. Briggman trusts overall

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

1. Pablo Arbeláez
2. Yann LeCun
3. David R. Martin
4. Jitendra Malik
5. Pedro F. Felzenszwalb
6. Charless C. Fowlkes
7. Yuri Boykov
8. Jianbo Shi
9. Geoffrey E. Hinton
10. Yoshua Bengio

Who trusts Kevin L. Briggman overall

1. Moritz Helmstaedter
2. Eve Marder
3. Viren Jain
4. Albert Cardona
5. Srinivas C. Turaga
6. Olaf Sporns
7. Richard H. Masland
8. Mojtaba Seyedhosseini
9. Tao Zeng
10. Ignacio Arganda-Carreras

Papers from Kevin L. Briggman's perspective

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

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

Their papers

• Supervised Learning of Image Restoration with Convolutional Networks (2007) — 253 citations
• Maximin affinity learning of image segmentation (2009) — 150 citations
• Boundary Learning by Optimization with Topological Constraints (2010) — 155 citations
• Connectomic reconstruction of the inner plexiform layer in the mouse retina (2013) — 981 citations
• Wiring specificity in the direction-selectivity circuit of the retina (2011) — 833 citations
• Volume electron microscopy for neuronal circuit reconstruction. (2012) — 356 citations
• Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation (2010) — 408 citations