Jonathan Bragg

Artificial Intelligence · 2014–2021

A researcher in the Science corpus: 4 papers; trusts 62 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #23,793 of 35,345 by trust here, #16,471 by citations
• Natural Language Processing — 82 citations into it
• Evaluation & Benchmarks — 64 citations into it; #1,757 of 3,147 by trust here, #1,728 by citations
• Computer Vision and Pattern Recognition — 43 citations into it
• Computer Science Applications — 17 citations into it
• Large Language Models — 11 citations into it
• Prompting & Reasoning — 11 citations into it

Whom Jonathan Bragg trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Oriol Vinyals
5. Ilya Sutskever
6. Alex Krizhevsky
7. Yann LeCun
8. Kaiming He
9. Ido Dagan
10. Alex Graves

Who trusts Jonathan Bragg overall

1. Katikapalli Subramanyam Kalyan
2. Ana Marasovic
3. Albert Webson
4. Abhilasha Ravichander
5. Iz Beltagy
6. Ruiqi Zhong
7. Wen Xiao
8. Daniel Khashabi
9. Ajit Rajasekharan
10. Ellie Pavlick

Papers from Jonathan Bragg's perspective

Ordered by how strongly the researchers Jonathan Bragg 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. 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. ImageNet: A large-scale hierarchical image database
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• FLEX: Unifying Evaluation for Few-Shot NLP (2021) — 113 citations
• Effective Crowd Annotation for Relation Extraction (2016) — 83 citations
• G ENIE A Leaderboard for Human-in-the-Loop Evaluation of Text Generation (2021)
• Artificial Intelligence and Collective Intelligence (2014) — 34 citations