Karin M. Verspoor
Artificial Intelligence · with Lu · 2012–2018
A researcher in the Science corpus: 4 papers; trusts 75 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 118 citations into it; #33,196 of 35,345 by trust here, #4,923 by citations
- Natural Language Processing — 97 citations into it; #5,431 of 5,688 by trust here, #1,351 by citations
- Molecular Biology — 51 citations into it; #15,537 of 17,773 by trust here, #11,019 by citations
- Evaluation & Benchmarks — 47 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it
- Model Architectures — 10 citations into it
- Information Extraction — 5 citations into it
Whom Karin M. Verspoor trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Timothy Dozat
- Christopher D. Manning
- Jin-Dong Kim
- Sepp Hochreiter
- Tomas Mikolov
- Geoffrey E. Hinton
- Ilya Sutskever
- Alex Graves
- Yoshua Bengio
- J. Urgen Schmidhuber
Who trusts Karin M. Verspoor overall
Papers from Karin M. Verspoor's perspective
Ordered by how strongly the researchers Karin M. Verspoor trusts endorse each paper. Same corpus, a different order for every researcher.
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
Their papers
- Findings of the 2016 Conference on Machine Translation (2016) — 838 citations
- The CHEMDNER corpus of chemicals and drugs and its annotation principles (2015) — 444 citations
- From POS tagging to dependency parsing for biomedical event extraction (2018) — 36 citations
- Concept annotation in the CRAFT corpus (2012) — 304 citations