Peter Shaw

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 5 papers; trusts 116 researchers and is trusted by 440 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 149 citations into it; #1,858 of 35,345 by trust here, #2,975 by citations
• Natural Language Processing — 99 citations into it; #4,880 of 5,688 by trust here, #3,687 by citations
• Computer Vision and Pattern Recognition — 75 citations into it; #2,081 of 21,575 by trust here, #2,322 by citations
• Model Architectures — 21 citations into it; #235 of 2,278 by trust here, #383 by citations
• Computational Linguistics & Semantics — 13 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Information Extraction — 8 citations into it

Whom Peter Shaw trusts overall

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

1. Brenden M. Lake
2. Diederik P. Kingma
3. Jacob Andreas
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Jimmy Ba
7. Ilya Sutskever
8. Alex Krizhevsky
9. Yann LeCun
10. Alex Graves

Who trusts Peter Shaw overall

1. Yu-Siang Huang
2. Irwan Bello
3. Pengcheng He
4. Quoc V. Le
5. Surafel Melaku Lakew
6. J. Ainslie
7. Xiangxiang Chu
8. Jianfeng Gao
9. Jaehyeon Kim
10. Sanyuan Chen

Papers from Peter Shaw's perspective

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

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

Their papers

• Self-Attention with Relative Position Representations (2018) — 2,875 citations
• Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both? (2020) — 197 citations
• Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding (2022) — 473 citations
• Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations (2021) — 72 citations
• Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing (2022) — 60 citations