Ethan Gotlieb Wilcox

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 87 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it; #10,451 of 35,345 by trust here, #10,566 by citations
• Natural Language Processing — 71 citations into it; #2,182 of 5,688 by trust here, #2,660 by citations
• Cognitive Neuroscience — 31 citations into it; #9,102 of 10,852 by trust here, #6,526 by citations
• Model Architectures — 28 citations into it
• Developmental and Educational Psychology — 25 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Large Language Models — 11 citations into it

Whom Ethan Gotlieb Wilcox trusts overall

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

1. M. Masson
2. Roger Levy
3. Yoav Goldberg
4. Jeffrey L. Elman
5. Ilya Sutskever
6. Tal Linzen
7. John Hale
8. R. Harald Baayen
9. Oriol Vinyals
10. Sepp Hochreiter

Who trusts Ethan Gotlieb Wilcox overall

1. Alex Warstadt
2. Tal Linzen
3. Jennifer Hu
4. Peng Qian
5. Richard Futrell
6. R. Thomas McCoy
7. Aaron Mueller
8. Samuel R. Bowman
9. Sebastian Schuster
10. Marten van Schijndel

Papers from Ethan Gotlieb Wilcox's perspective

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

1. Long Short-Term Memory
2. Attention is All you Need (Neural Information Processing Systems)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
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
• A Systematic Assessment of Syntactic Generalization in Neural Language Models (2020) — 275 citations
• Neural language models as psycholinguistic subjects: Representations of syntactic state (2019) — 237 citations