Ted Underwood

Natural Language Processing · 2014

A researcher in the Science corpus: 1 paper; trusts 19 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 11 citations into it; #2,307 of 5,688 by trust here, #4,793 by citations
• Artificial Intelligence — 9 citations into it; #12,018 of 35,345 by trust here, #26,682 by citations

Whom Ted Underwood trusts overall

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

1. R. Rosenfeld
2. Joshua Goodman
3. Stanley F. Chen
4. Frederic Morin
5. Tomas Mikolov
6. Galen Andrew
7. Yoshua Bengio
8. Jenny Rose Finkel
9. David Bamman
10. David K. Elson

Who trusts Ted Underwood overall

1. Snigdha Chaturvedi
2. Lea Frermann
3. Philip John Gorinski
4. Li Lucy
5. Weiwei Zhang
6. David Bamman

Papers from Ted Underwood's perspective

Ordered by how strongly the researchers Ted Underwood 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. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Efficient Estimation of Word Representations in Vector Space
8. ImageNet: A large-scale hierarchical image database
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

• A Bayesian Mixed Effects Model of Literary Character (2014) — 229 citations