Cody Watson

Information Systems · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 68 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 59 citations into it; #6,294 of 8,771 by trust here, #2,064 by citations
• Artificial Intelligence — 45 citations into it
• Software — 45 citations into it; #1,072 of 1,227 by trust here, #304 by citations
• Natural Language Processing — 32 citations into it; #5,180 of 5,688 by trust here, #3,529 by citations
• Experimental and Cognitive Psychology — 29 citations into it
• Signal Processing — 16 citations into it
• Model Architectures — 15 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it

Whom Cody Watson trusts overall

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

1. Ilya Sutskever
2. Yoshua Bengio
3. Alex Graves
4. Veselin Raychev
5. Sepp Hochreiter
6. Oriol Vinyals
7. Dzmitry Bahdanau
8. Kyunghyun Cho
9. Geoffrey E. Hinton
10. Philipp Koehn

Who trusts Cody Watson overall

1. Michele Tufano
2. Max Schäfer
3. Baptiste Rozière
4. Zhiqiang Yuan
5. Naman Jain
6. Dawn Drain
7. Shaked Brody
8. Matteo Ciniselli
9. Priyan Vaithilingam
10. Sarah Nadi

Papers from Cody Watson's perspective

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

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
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. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Their papers

• On Learning Meaningful Assert Statements for Unit Test Cases (2020) — 196 citations
• On Learning Meaningful Code Changes Via Neural Machine Translation (2019) — 249 citations
• An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation (2018) — 450 citations
• Creative metacognition and self-ratings of creative performance: A 4-C perspective (2016) — 108 citations