Bevan Koopman

Artificial Intelligence · 2014–2026

A researcher in the Science corpus: 4 papers; trusts 18 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 23 citations into it; #15,992 of 35,345 by trust here, #21,316 by citations
• Natural Language Processing — 20 citations into it; #2,999 of 5,688 by trust here, #4,682 by citations
• Representation & Self-Supervised Learning — 14 citations into it
• Information Retrieval & Search — 11 citations into it; #521 of 1,022 by trust here, #748 by citations
• Information Systems — 7 citations into it; #3,349 of 8,771 by trust here, #5,942 by citations

Whom Bevan Koopman trusts overall

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

1. Tomas Mikolov
2. Chengxiang Zhai
3. Ellen M. Voorhees
4. John D. Lafferty
5. Yoav Goldberg
6. Yoshua Bengio
7. Adam L. Berger
8. Jeffrey Pennington
9. Ronan Collobert
10. Claudio Carpineto

Who trusts Bevan Koopman overall

1. Hamed Zamani
2. Navid Rekabsaz
3. W. Bruce Croft
4. Jiafeng Guo
5. Qingyao Ai
6. Saar Kuzi
7. Mihai Lupu
8. Kezban Dilek Onal

Papers from Bevan Koopman's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. ImageNet classification with deep convolutional neural networks
3. Long Short-Term Memory
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. GloVe: Global Vectors for Word Representation
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Efficient Estimation of Word Representations in Vector Space
10. ImageNet: A large-scale hierarchical image database

Their papers

• Medical Semantic Similarity with a Neural Language Model (2014) — 161 citations
• Beyond Chunk-Then-Embed: A Comprehensive Taxonomy and Evaluation of Document Chunking Strategies for Information Retrieval (2026)
• Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking (2023)
• Integrating and Evaluating Neural Word Embeddings in Information Retrieval (2015) — 137 citations