Barry Smyth

Artificial Intelligence · 2001–2018

A researcher in the Science corpus: 15 papers; trusts 106 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #11,177 of 35,345 by trust here, #13,760 by citations
• Information Systems — 63 citations into it; #1,046 of 8,771 by trust here, #1,069 by citations
• Natural Language Processing — 45 citations into it
• Computer Vision and Pattern Recognition — 37 citations into it; #7,744 of 21,575 by trust here, #11,950 by citations
• Recommender Systems — 30 citations into it
• Statistical and Nonlinear Physics — 14 citations into it
• Model Architectures — 13 citations into it
• Sociology and Political Science — 13 citations into it

Whom Barry Smyth trusts overall

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

1. Ruslan Salakhutdinov
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Jonathan L. Herlocker
6. Tomas Mikolov
7. Ronald J. Williams
8. Alex Graves
9. Paolo Massa
10. Diederik P. Kingma

Who trusts Barry Smyth overall

1. Li Chen
2. Marius Kaminskas
3. Charu C. Aggarwal
4. Derek G. Bridge
5. Neal Kiritkumar Lathia
6. Yongfeng Zhang
7. Sophie Ahrens
8. Mohsen Jamali
9. Xiwang Yang
10. Francesco Ricci

Papers from Barry Smyth's perspective

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

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

Their papers

• Trust in recommender systems (2005) — 978 citations
• Recommending twitter users to follow using content and collaborative filtering approaches (2010) — 509 citations
• Explanation-based Ranking in Opinionated Recommender Systems (2018) — 4 citations
• Why I like it: multi-task learning for recommendation and explanation (2018) — 147 citations
• Coevolutionary Recommendation Model: Mutual Learning between Ratings and Reviews (2018) — 170 citations
• On the Use of Opinionated Explanations to Rank and Justify Recommendations (2016) — 10 citations
• A Live-User Study of Opinionated Explanations for Recommender Systems (2016) — 50 citations
• Great Explanations: Opinionated Explanations for Recommendations (2015) — 43 citations
• Opinionated Explanations for Recommendation Systems (2015) — 10 citations
• Terms of a Feather: Content-Based News Recommendation and Discovery Using Twitter (2011) — 119 citations
• Using twitter to recommend real-time topical news (2009) — 470 citations
• Recommendation to Groups (2007) — 440 citations
• Case Studies in Association Rule Mining for Recommender Systems (2005) — 22 citations
• Case-based recommender systems (2005) — 301 citations
• Similarity vs. Diversity (2001) — 480 citations