Wilson S. Geisler
Cognitive Neuroscience · 1991–2009
A researcher in the Science corpus: 7 papers; trusts 48 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 50 citations into it; #692 of 10,852 by trust here, #1,172 by citations
- Social Psychology — 28 citations into it
- Experimental and Cognitive Psychology — 14 citations into it
- Cellular and Molecular Neuroscience — 11 citations into it; #2,733 of 2,998 by trust here, #2,267 by citations
- Developmental and Educational Psychology — 11 citations into it
- Computer Vision and Pattern Recognition — 10 citations into it; #4,725 of 21,575 by trust here, #5,205 by citations
- Cognitive Science & Psychology — 8 citations into it
- Neuroscience — 8 citations into it
Whom Wilson S. Geisler trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David J. Field
- Simon Barry Laughlin
- F. Attneave
- H. Barlow
- Ann B. Lee
- Anthony J. Bell
- Eero P. Simoncelli
- Mark E. Wheeler
- Daniel L. Ruderman
- Barbara Olshausen
Who trusts Wilson S. Geisler overall
- Eero P. Simoncelli
- David J. Heeger
- Sophie Denève
- Adam N. Sanborn
- Zhou Wang
- Matteo Carandini
- A. Bovik
- John H. Reynolds
- Damon M. Chandler
- James H. Elder
Papers from Wilson S. Geisler's perspective
Ordered by how strongly the researchers Wilson S. Geisler trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Relations between the statistics of natural images and the response properties of cortical cells.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Image quality assessment based on a degradation model (2000) — 923 citations
- Edge co-occurrence in natural images predicts contour grouping performance. (2001) — 712 citations
- Optimal eye movement strategies in visual search (2005) — 812 citations
- Complex dynamics of V1 population responses explained by a simple gain-control model (2009) — 79 citations
- Visual perception and the statistical properties of natural scenes. (2008) — 713 citations
- Independence of luminance and contrast in natural scenes and in the early visual system (2005) — 393 citations
- Motion selectivity and the contrast-response function of simple cells in the visual cortex (1991)