Yi Luan
Artificial Intelligence · with Hajishirzi · 2017–2019
A researcher in the Science corpus: 6 papers; trusts 157 researchers and is trusted by 31 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 113 citations into it; #6,315 of 35,345 by trust here, #6,720 by citations
- Natural Language Processing — 90 citations into it; #2,566 of 5,688 by trust here, #2,660 by citations
- Evaluation & Benchmarks — 23 citations into it
- Computer Vision and Pattern Recognition — 18 citations into it
- Molecular Biology — 18 citations into it
- Representation & Self-Supervised Learning — 15 citations into it
- Model Architectures — 14 citations into it
- Optimization & Training — 7 citations into it
Whom Yi Luan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Sepp Hochreiter
- Geoffrey E. Hinton
- Yoshua Bengio
- Kenton C. T. Lee
- Ilya Sutskever
- Alex Graves
- Oriol Vinyals
- J. Urgen Schmidhuber
- Alex Krizhevsky
Who trusts Yi Luan overall
- Zexuan Zhong
- Markus Eberts
- David Wadden
- M. V. Koroteev
- Danqi Chen
- Adrian Ulges
- Leonardo F. R. Ribeiro
- Tong Niu
- Hannaneh Hajishirzi
- Iz Beltagy
Papers from Yi Luan's perspective
Ordered by how strongly the researchers Yi Luan trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (2018) — 789 citations
- Text Generation from Knowledge Graphs with Graph Transformers (2019) — 363 citations
- Entity, Relation, and Event Extraction with Contextualized Span Representations (2019) — 684 citations
- Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models (2017) — 81 citations
- A general framework for information extraction using dynamic span graphs (2019) — 342 citations
- PaperRobot: Incremental Draft Generation of Scientific Ideas (2019) — 75 citations