Mark Andrews

Cognitive Neuroscience · 2009–2011

A researcher in the Science corpus: 3 papers; trusts 15 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 37 citations into it
• Social Psychology — 34 citations into it; #8,610 of 15,694 by trust here, #6,804 by citations
• Developmental and Educational Psychology — 30 citations into it; #1,949 of 8,223 by trust here, #1,995 by citations
• Experimental and Cognitive Psychology — 26 citations into it; #5,317 of 9,731 by trust here, #3,963 by citations
• Natural Language Processing — 14 citations into it
• Artificial Intelligence — 13 citations into it; #15,073 of 35,345 by trust here, #17,412 by citations
• Representation & Self-Supervised Learning — 9 citations into it

Whom Mark Andrews trusts overall

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

1. T. Landauer
2. Thomas Hofmann
3. Hinrich Schütze
4. David M. Blei
5. Frank C. Keil
6. Susan T. Dumais
7. Jean M. Mandler
8. Andrew Y. Ng
9. Lawrence W. Barsalou
10. Arthur M. Glenberg

Who trusts Mark Andrews overall

1. Mirella Lapata
2. Carina Silberer
3. Gabriella Vigliocco
4. Yansong Feng
5. Li Lucy
6. S. Kousta
7. Stephen J. Flusberg
8. Guy Dove
9. Marc Brysbaert
10. Alessandro Lenci

Papers from Mark Andrews's perspective

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

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

Their papers

• Integrating experiential and distributional data to learn semantic representations. (2009) — 440 citations
• Toward a theory of semantic representation (2009) — 316 citations
• The representation of abstract words: why emotion matters. (2011) — 802 citations