Winfried Denk
Computer Vision and Pattern Recognition · 2000–2017
A researcher in the Science corpus: 13 papers; trusts 123 researchers and is trusted by 24 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 60 citations into it; #14,459 of 21,575 by trust here, #7,249 by citations
- Biophysics — 31 citations into it; #193 of 1,196 by trust here, #98 by citations
- Cellular and Molecular Neuroscience — 17 citations into it; #659 of 2,998 by trust here, #386 by citations
- Molecular Biology — 14 citations into it; #4,253 of 17,773 by trust here, #4,696 by citations
- Structural Biology — 14 citations into it
- Neuroscience — 13 citations into it
- Surfaces, Coatings and Films — 10 citations into it
- Biomedical Engineering — 9 citations into it; #2,103 of 10,734 by trust here, #2,774 by citations
Whom Winfried Denk trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yann LeCun
- A. Nimmerjahn
- J. Grutzendler
- Pablo Arbeláez
- David R. Martin
- Rakesh K. Jain
- Jitendra Malik
- Yi Zuo
- Geoffrey E. Hinton
- Charless C. Fowlkes
Who trusts Winfried Denk overall
- Moritz Helmstaedter
- Karel Svoboda
- Anthony Holtmaat
- Michael A. D. Taylor
- Liqun Luo
- H. Wässle
- Viren Jain
- Vasilis Ntziachristos
- Richard H. Masland
- Warwick P. Bowen
Papers from Winfried Denk's perspective
Ordered by how strongly the researchers Winfried Denk trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- ImageNet classification with deep convolutional neural networks
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- 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
- Deep tissue two-photon microscopy (2005) — 4,131 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
- Directionally selective calcium signals in dendrites of starburst amacrine cells (2002) — 587 citations
- A miniature head-mounted two-photon microscope. high-resolution brain imaging in freely moving animals. (2001) — 617 citations
- High-precision automated reconstruction of neurons with flood-filling networks (2017) — 380 citations
- Grand Challenges for Global Brain Sciences (2016)
- Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation (2010) — 408 citations
- Two-photon imaging to a depth of 1000 microm in living brains by use of a Ti:Al2O3 regenerative amplifier. (2003) — 631 citations
- Coincidence detection in single dendritic spines mediated by calcium release (2000) — 493 citations