Richard Futrell

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 7 papers; trusts 76 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it; #10,406 of 35,345 by trust here, #11,750 by citations
• Natural Language Processing — 56 citations into it; #2,119 of 5,688 by trust here, #2,634 by citations
• Cognitive Neuroscience — 45 citations into it; #9,097 of 10,852 by trust here, #6,180 by citations
• Developmental and Educational Psychology — 41 citations into it
• Language and Linguistics — 21 citations into it
• Model Architectures — 21 citations into it
• Statistics and Probability — 16 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it

Whom Richard Futrell trusts overall

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

1. M. Masson
2. Yoav Goldberg
3. Jeffrey L. Elman
4. Ilya Sutskever
5. Roger Levy
6. Oriol Vinyals
7. Geoffrey E. Hinton
8. R. Harald Baayen
9. Tomas Mikolov
10. Yoshua Bengio

Who trusts Richard Futrell overall

1. Alex Warstadt
2. Ethan Gotlieb Wilcox
3. Jennifer Hu
4. Tal Linzen
5. Sebastian Schuster
6. Rajakrishnan Rajkumar
7. Peng Qian
8. Marten van Schijndel
9. Benjamin Newman
10. Dorottya Demszky

Papers from Richard Futrell's perspective

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

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

Their papers

• What do RNN Language Models Learn about Filler–Gap Dependencies? (2018) — 206 citations
• Structural Supervision Improves Learning of Non-Local Grammatical Dependencies (2019) — 61 citations
• RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency (2018) — 64 citations
• Neural language models as psycholinguistic subjects: Representations of syntactic state (2019) — 237 citations
• When classifying arguments, BERT doesn’t care about word order...except when it matters (2022)
• A meta-analysis of syntactic priming in language production (2016) — 252 citations
• Large-scale evidence of dependency length minimization in 37 languages (2015) — 412 citations