Preethi Raghavan
Artificial Intelligence · 2013–2018
A researcher in the Science corpus: 3 papers; trusts 20 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 44 citations into it
- Molecular Biology — 25 citations into it
- Natural Language Processing — 21 citations into it
- Health Information Management — 13 citations into it
- Computer Vision and Pattern Recognition — 10 citations into it
Whom Preethi Raghavan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Dina Demner-Fushman
- Alan R. Aronson
- Ilya Sutskever
- Ozlem Uzuner
- Wendy W. Chapman
- Yoshua Bengio
- Dzmitry Bahdanau
- Antoine Bordes
- Geoffrey E. Hinton
- Robin Jia
Who trusts Preethi Raghavan overall
Papers from Preethi Raghavan's perspective
Ordered by how strongly the researchers Preethi Raghavan trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- 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
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- emrQA: A Large Corpus for Question Answering on Electronic Medical Records (2018) — 264 citations
- How essential are unstructured clinical narratives and information fusion to clinical trial recruitment? (2014) — 54 citations
- A review of approaches to identifying patient phenotype cohorts using electronic health records (2013) — 528 citations