Paul McNamee

Artificial Intelligence · 2004–2019

A researcher in the Science corpus: 7 papers; trusts 69 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it; #10,093 of 35,345 by trust here, #12,974 by citations
• Natural Language Processing — 71 citations into it
• Computer Vision and Pattern Recognition — 40 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Optimization & Training — 17 citations into it
• Management Science and Operations Research — 9 citations into it; #6,854 of 11,946 by trust here, #9,383 by citations
• Model Architectures — 8 citations into it
• Information Retrieval & Search — 7 citations into it

Whom Paul McNamee trusts overall

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

1. Djoerd Hiemstra
2. Thorsten Joachims
3. Franz Joseph Och
4. Peter Christen
5. Gerard A. Salton
6. Chengxiang Zhai
7. Diederik P. Kingma
8. Yoshua Bengio
9. Andrew McCallum
10. Philipp Koehn

Who trusts Paul McNamee overall

1. Cristina España-Bonet
2. Dirk Hovy
3. Alberto Barrón-Cedeño
4. Jérémy Ferrero
5. Zhaochen Guo
6. Margaret Mitchell
7. Guanghui Qin
8. Laurent Besacier
9. Á. Varga
10. Zhilin Yang

Papers from Paul McNamee's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. ImageNet classification with deep convolutional neural networks
6. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Gradient-based learning applied to document recognition
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Entity Linking: Finding Extracted Entities in a Knowledge Base (2013) — 224 citations
• Curriculum Learning for Domain Adaptation in Neural Machine Translation (2019) — 131 citations
• Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation (2018) — 56 citations
• An Empirical Exploration of Curriculum Learning for Neural Machine Translation (2018) — 125 citations
• Identifying Fluently Inadequate Output in Neural and Statistical Machine Translation (2019) — 34 citations
• Entity Disambiguation for Knowledge Base Population (2010) — 353 citations
• Character N-Gram Tokenization for European Language Text Retrieval (2004) — 388 citations