Jeremy M. Wolfe
Cognitive Neuroscience · 2003–2021
A researcher in the Science corpus: 8 papers; trusts 103 researchers and is trusted by 37 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 99 citations into it; #635 of 10,852 by trust here, #1,588 by citations
- Computer Vision and Pattern Recognition — 47 citations into it; #8,256 of 21,575 by trust here, #11,950 by citations
- Cognitive Science & Psychology — 20 citations into it
- Neuroscience — 19 citations into it
- Experimental and Cognitive Psychology — 17 citations into it; #1,133 of 9,731 by trust here, #4,316 by citations
- Social Psychology — 11 citations into it; #3,490 of 15,694 by trust here, #3,895 by citations
- Sensory Systems — 10 citations into it; #53 of 760 by trust here, #347 by citations
- Molecular Biology — 6 citations into it; #5,403 of 17,773 by trust here, #13,284 by citations
Whom Jeremy M. Wolfe trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Anne Treisman
- John Palmer
- Dan Ariely
- Philipp Koehn
- Marvin M. Chun
- Aude Oliva
- Nelson Cowan
- Sang Chul Chong
- Antonio Torralba
- Garry A. Gelade
Who trusts Jeremy M. Wolfe overall
- Ali Borji
- Laurent Itti
- Simone Frintrop
- Miguel P. Eckstein
- Pierre Baldi
- Rick T. Wilson
- Erich Rome
- Penghao Wu
- A. Ohman
- Ming-Ming Cheng
Papers from Jeremy M. Wolfe's perspective
Ordered by how strongly the researchers Jeremy M. Wolfe 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Going deeper with convolutions
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Five Factors that Guide Attention in Visual Search (2017) — 787 citations
- What attributes guide the deployment of visual attention and how do they do it? (2004) — 1,875 citations
- Guided Search 4.0: Current Progress With a Model of Visual Search (2007) — 809 citations
- Guided Search 6.0: An updated model of visual search (2021) — 536 citations
- Forty years after Feature Integration Theory (2020) — 20 citations
- Visual search (2010) — 1,072 citations
- Why don't we see changes? The role of attentional bottlenecks and limited visual memory (2006) — 68 citations
- Memory for rejected distractors in visual search? (2003) — 112 citations