Robert Leaman
Molecular Biology · 2007–2019
A researcher in the Science corpus: 11 papers; trusts 14 researchers and is trusted by 44 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 69 citations into it; #684 of 17,773 by trust here, #2,948 by citations
- Artificial Intelligence — 46 citations into it; #4,747 of 35,345 by trust here, #5,533 by citations
- Computational Theory and Mathematics — 17 citations into it; #11,612 of 12,182 by trust here, #2,489 by citations
- Genetics — 13 citations into it; #3,247 of 6,854 by trust here, #2,594 by citations
- Natural Language Processing — 12 citations into it; #3,553 of 5,688 by trust here, #2,497 by citations
- Computational Biology & Bioinformatics — 10 citations into it
- Information Extraction — 10 citations into it
- Statistics and Probability — 7 citations into it
Whom Robert Leaman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Zhiyong Lu
- Chih-Hsuan Wei
- Koby Crammer
- Andrew McCallum
- Rezarta Islamaj Dogan
- Alan R. Aronson
- Graciela Gonzalez
- Burr Settles
- Eric R. Ziegel
- Yoram Singer
Who trusts Robert Leaman overall
- Chih-Hsuan Wei
- Zhiyong Lu
- Yifan Peng
- Rezarta Islamaj Dogan
- Jiao Li
- Maryam Habibi
- WonJin Yoon
- Fei Wang
- Nathan Greenberg
- Jinghang Gu
Papers from Robert Leaman's perspective
Ordered by how strongly the researchers Robert Leaman trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- BioCreative V CDR task corpus: a resource for chemical disease relation extraction (2016) — 1,061 citations
- Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task (2016) — 357 citations
- The CHEMDNER corpus of chemicals and drugs and its annotation principles (2015) — 444 citations
- BANNER: An Executable Survey of Advances in Biomedical Named Entity Recognition (2007) — 578 citations
- DNorm: disease name normalization with pairwise learning to rank (2013) — 517 citations
- tmChem: a high performance approach for chemical named entity recognition and normalization (2015) — 300 citations
- NCBI disease corpus: A resource for disease name recognition and concept normalization (2014) — 945 citations
- Overview of BioCreative II gene normalization (2008) — 370 citations
- TaggerOne: joint named entity recognition and normalization with semi-Markov Models (2016) — 293 citations
- PubTator central: automated concept annotation for biomedical full text articles (2019) — 383 citations
- Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks (2010) — 314 citations