Jeffrey S. Bowers

Cognitive Neuroscience · with Davis · 2002–2018

A researcher in the Science corpus: 11 papers; trusts 131 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 59 citations into it; #1,323 of 10,852 by trust here, #3,180 by citations
• Developmental and Educational Psychology — 50 citations into it; #4,135 of 8,223 by trust here, #2,262 by citations
• Artificial Intelligence — 35 citations into it
• Neuroscience — 19 citations into it
• Statistics and Probability — 18 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Education — 16 citations into it
• Experimental and Cognitive Psychology — 16 citations into it

Whom Jeffrey S. Bowers trusts overall

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

1. James L. McClelland
2. Geoffrey E. Hinton
3. David E. Rumelhart
4. Stephen Grossberg
5. R. Quian Quiroga
6. J. Rose
7. Colin J. Davis
8. P. Lennie
9. Matthew Botvinick
10. Mike Page

Who trusts Jeffrey S. Bowers overall

1. Colin J. Davis
2. James L. McClelland
3. Phillip J. Holcomb
4. Markus Kiefer
5. Jonathan Grainger
6. Yair Lakretz
7. Friedemann Pulvermüller
8. R. Harald Baayen
9. Manuel Perea
10. Daniel Mirman

Papers from Jeffrey S. Bowers's perspective

Ordered by how strongly the researchers Jeffrey S. Bowers trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• What a difference a pixel makes: An empirical examination of features used by CNNs for categorisation (2018) — 3 citations
• Reconsidering the Evidence That Systematic Phonics Is More Effective Than Alternative Methods of Reading Instruction (2018) — 90 citations
• Beyond Phonics: The Case for Teaching Children the Logic of the English Spelling System (2017) — 104 citations
• Grandmother cells and localist representations: a review of current thinking (2017) — 28 citations
• Neural networks learn highly selective representations in order to overcome the superposition catastrophe. (2014) — 28 citations
• Bayesian just-so stories in psychology and neuroscience. (2012) — 405 citations
• On the biological plausibility of grandmother cells: implications for neural network theories in psychology and neuroscience. (2009) — 215 citations
• Automatic semantic activation of embedded words: Is there a hat in that? (2005) — 136 citations
• Effects of orthography on speech production in a form-preparation paradigm (2003) — 145 citations
• Masked priming: State of the Art (2003) — 187 citations
• Rethinking Implicit Memory (2002) — 103 citations