Guokun Lai

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 5 papers; trusts 61 researchers and is trusted by 75 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 80 citations into it; #3,987 of 35,345 by trust here, #2,668 by citations
• Natural Language Processing — 50 citations into it; #1,172 of 5,688 by trust here, #860 by citations
• Information Systems — 39 citations into it; #2,054 of 8,771 by trust here, #1,045 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Evaluation & Benchmarks — 19 citations into it; #718 of 3,147 by trust here, #540 by citations
• Management Science and Operations Research — 14 citations into it; #4,690 of 11,946 by trust here, #1,654 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Model Architectures — 10 citations into it

Whom Guokun Lai trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yann LeCun
6. Ilya Sutskever
7. Jimmy Ba
8. Sepp Hochreiter
9. J. Urgen Schmidhuber
10. Ruslan Salakhutdinov

Who trusts Guokun Lai overall

1. Yongfeng Zhang
2. Yiming Cui
3. Kai Sun
4. Yi Tay
5. Daniel Khashabi
6. Xu Chen
7. Saku Sugawara
8. Haixu Wu
9. Marco Valentino
10. Oyvind Tafjord

Papers from Guokun Lai's perspective

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

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

Their papers

• RACE: Large-scale ReAding Comprehension Dataset From Examinations (2017) — 1,655 citations
• Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (2014) — 858 citations
• Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks (2017) — 2,865 citations
• Bridging the domain gap in cross-lingual document classification (2019) — 16 citations
• Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis (2015) — 80 citations