Nathan Cooper

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it
• Natural Language Processing — 27 citations into it
• Model Architectures — 7 citations into it

Whom Nathan Cooper trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Chin-Yew Lin
4. Kaiming He
5. Jimmy Ba
6. Yoshua Bengio
7. Ilya Sutskever
8. Alex Krizhevsky

Papers from Nathan Cooper's perspective

Ordered by how strongly the researchers Nathan Cooper 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. Long Short-Term Memory
5. Dropout: a simple way to prevent neural networks from overfitting
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Layer Normalization
8. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
9. Gradient-based learning applied to document recognition
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks (2021) — 285 citations