Kevin Jesse

Artificial Intelligence · 2021–2023

A researcher in the Science corpus: 2 papers; trusts 58 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it; #21,460 of 35,345 by trust here, #29,412 by citations
• Natural Language Processing — 34 citations into it; #3,850 of 5,688 by trust here, #5,296 by citations
• Information Systems — 31 citations into it; #4,569 of 8,771 by trust here, #7,107 by citations
• Software — 19 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Program Analysis & Verification — 17 citations into it; #127 of 301 by trust here, #235 by citations
• Large Language Models — 13 citations into it
• Code Generation & Program Synthesis — 11 citations into it

Whom Kevin Jesse trusts overall

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

1. Miltiadis Allamanis
2. Taku Kudo
3. Veselin Raychev
4. Rico Sennrich
5. Jacob Devlin
6. Geoffrey E. Hinton
7. Earl T. Barr
8. Yoshua Bengio
9. Vincent J. Hellendoorn
10. Iasonas Kokkinos

Who trusts Kevin Jesse overall

1. Ming-Ho Yee
2. Amir M. Mir
3. Arjun Guha
4. Prem Devanbu
5. Daniel Fried
6. Evaldas Latoskinas
7. Toufique Ahmed

Papers from Kevin Jesse's perspective

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

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

Their papers

• Learning type annotation: is big data enough? (2021) — 43 citations
• Learning to Predict User-Defined Types (2023) — 13 citations