Luchen Tan

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 6 papers; trusts 37 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #15,223 of 35,345 by trust here, #7,172 by citations
• Natural Language Processing — 85 citations into it
• Computer Vision and Pattern Recognition — 50 citations into it; #11,458 of 21,575 by trust here, #5,987 by citations
• Information Retrieval & Search — 46 citations into it; #500 of 1,022 by trust here, #228 by citations
• Evaluation & Benchmarks — 29 citations into it
• Large Language Models — 18 citations into it
• Information Systems — 17 citations into it
• General Social Sciences — 6 citations into it

Whom Luchen Tan trusts overall

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

1. Alec Radford
2. Jacob Devlin
3. Geoffrey E. Hinton
4. Jimmy J. Lin
5. Yoshua Bengio
6. Diederik P. Kingma
7. Peilin Yang
8. Richard Socher
9. Wei Yang
10. Ilya Sutskever

Who trusts Luchen Tan overall

1. Wei Yang
2. Zeynep Akkalyoncu Yilmaz
3. Wenhan Xiong
4. Jimmy J. Lin
5. Yuqing Xie
6. Wissam Siblini
7. Jinhyuk Lee

Papers from Luchen Tan's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Gradient-based learning applied to document recognition
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• End-to-End Open-Domain Question Answering with BERTserini (2019) — 526 citations
• Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering (2019) — 70 citations
• Segatron: Segment-Aware Transformer for Language Modeling and Understanding (2020) — 25 citations
• Distant Supervision for Multi-Stage Fine-Tuning in Retrieval-Based Question Answering (2020) — 23 citations
• Approximate Nearest Neighbor Search and Lightweight Dense Vector Reranking in Multi-Stage Retrieval Architectures (2020) — 17 citations
• Overview of the TREC 2016 Real-Time Summarization Track (2016) — 79 citations