Robert Frank

Artificial Intelligence · 2001–2019

A researcher in the Science corpus: 4 papers; trusts 30 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #9,047 of 35,345 by trust here, #11,921 by citations
• Natural Language Processing — 31 citations into it; #3,661 of 5,688 by trust here, #3,235 by citations
• Model Architectures — 14 citations into it; #1,850 of 2,278 by trust here, #2,215 by citations
• Cognitive Neuroscience — 8 citations into it; #5,068 of 10,852 by trust here, #4,260 by citations
• Cognitive Science & Psychology — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Developmental and Educational Psychology — 6 citations into it
• Computational Linguistics & Semantics — 5 citations into it; #209 of 278 by trust here, #259 by citations

Whom Robert Frank trusts overall

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

1. Jeffrey L. Elman
2. Paul Smolensky
3. P. Rodríguez
4. Matthew Botvinick
5. Gary F. Marcus
6. David C. Plaut
7. Ilya Sutskever
8. Geoffrey E. Hinton
9. Mitchell P. Marcus
10. Diederik P. Kingma

Who trusts Robert Frank overall

1. Yonatan Belinkov
2. Tal Linzen
3. R. Thomas McCoy
4. Alex Warstadt
5. James R. Glass
6. Shauli Ravfogel
7. Brenden M. Lake
8. Yongjie Lin
9. Yada Pruksachatkun
10. Samuel R. Bowman

Papers from Robert Frank's perspective

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

1. Long Short-Term Memory
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Attention is All you Need (Neural Information Processing Systems)
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Open Sesame: Getting inside BERT’s Linguistic Knowledge (2019) — 314 citations
• Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks (2018) — 87 citations
• The Acquisition of Anaphora by Simple Recurrent Networks (2013) — 20 citations
• Primitive C‐Command (2001) — 50 citations