Terra Blevins

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 4 papers; trusts 73 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it
• Natural Language Processing — 54 citations into it; #3,170 of 5,688 by trust here, #3,478 by citations
• Computer Vision and Pattern Recognition — 35 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Evaluation & Benchmarks — 12 citations into it

Whom Terra Blevins trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Richard Socher
5. Ilya Sutskever
6. Jeffrey Pennington
7. Kenton C. T. Lee
8. Alex Krizhevsky
9. Christopher D. Manning
10. Yann LeCun

Who trusts Terra Blevins overall

1. Clara Meister
2. Christopher D. Manning
3. Yonatan Belinkov
4. Paul Soulos
5. David Marecek
6. Ian Tenney
7. John Hewitt
8. Jesse Vig
9. Ryan Cotterell
10. Kevin Clark

Papers from Terra Blevins's perspective

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

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

Their papers

• Detecting Pretraining Data from Large Language Models (2023) — 424 citations
• Demystifying Prompts in Language Models via Perplexity Estimation (2022) — 313 citations
• Few-shot Mining of Naturally Occurring Inputs and Outputs (2022)
• Deep RNNs Encode Soft Hierarchical Syntax (2018) — 118 citations