David C. Plaut

Cognitive Science & Psychology · 1987–2010

A researcher in the Science corpus: 9 papers; trusts 38 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Science & Psychology — 28 citations into it; #557 of 582 by trust here, #343 by citations
• Cognitive Neuroscience — 24 citations into it; #1,023 of 10,852 by trust here, #2,549 by citations
• Neuroscience — 22 citations into it
• Model Architectures — 19 citations into it
• Artificial Intelligence — 14 citations into it; #16,399 of 35,345 by trust here, #24,417 by citations
• Representation & Self-Supervised Learning — 14 citations into it
• Natural Language Processing — 13 citations into it; #1,783 of 5,688 by trust here, #3,014 by citations
• Developmental and Educational Psychology — 7 citations into it; #2,603 of 8,223 by trust here, #3,376 by citations

Whom David C. Plaut trusts overall

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

1. Geoffrey E. Hinton
2. David E. Rumelhart
3. James L. McClelland
4. Barak A. Pearlmutter
5. Ronald J. Williams
6. Paul J. Werbos
7. Michael I. Jordan
8. Shun-ichi Amari
9. Richard S. Sutton
10. Jeffrey L. Elman

Who trusts David C. Plaut overall

1. Matthew Botvinick
2. M. L. Lambon Ralph
3. Geoffrey E. Hinton
4. James L. McClelland
5. Ronan Collobert
6. Kathleen Rastle
7. Bernard Ans
8. Johannes C. Ziegler
9. Timothy T. Rogers
10. Randall C. O’Reilly

Papers from David C. Plaut's perspective

Ordered by how strongly the researchers David C. Plaut 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Short-term memory for serial order: a recurrent neural network model. (2006) — 380 citations
• Doing without schema hierarchies: a recurrent connectionist approach to normal and impaired routine sequential action. (2004) — 370 citations
• Approaches to cognitive modeling Letting structure emerge : connectionist and dynamical systems approaches to cognition (2010)
• Locating object knowledge in the brain: comment on Bowers's (2009) attempt to revive the grandmother cell hypothesis. (2010) — 34 citations
• An Improved Model of Semantic Similarity Based on Lexical Co-Occurrence (2005)
• Are non-semantic morphological effects incompatible with a distributed connectionist approach to lexical processing? (2000) — 408 citations
• Systematicity and specialization in semantics (1999)
• Understanding normal and impaired word reading: computational principles in quasi-regular domains. (1996)
• Learning sets of filters using back-propagation (1987)