Jey Han Lau
Artificial Intelligence · 2010–2020
A researcher in the Science corpus: 10 papers; trusts 262 researchers and is trusted by 38 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 124 citations into it; #7,256 of 35,345 by trust here, #10,798 by citations
- Natural Language Processing — 112 citations into it; #1,345 of 5,688 by trust here, #2,292 by citations
- Evaluation & Benchmarks — 37 citations into it; #1,278 of 3,147 by trust here, #2,369 by citations
- Computer Vision and Pattern Recognition — 30 citations into it
- Representation & Self-Supervised Learning — 18 citations into it; #1,674 of 3,145 by trust here, #2,587 by citations
- Large Language Models — 17 citations into it
- Model Architectures — 16 citations into it
- Information Retrieval & Search — 10 citations into it
Whom Jey Han Lau trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- David M. Blei
- Yoshua Bengio
- Diederik P. Kingma
- Christopher D. Manning
- Ilya Sutskever
- Alex Krizhevsky
- Tomas Mikolov
- Michael I. Jordan
- Ruslan Salakhutdinov
Who trusts Jey Han Lau overall
- Alex Warstadt
- Akash Srivastava
- Wenlin Wang
- Lea Frermann
- Michael Röder
- Timothy Baldwin
- Samuel R. Bowman
- Derek O'Callaghan
- Daniel Matthew Cer
- Mario Giulianelli
Papers from Jey Han Lau's perspective
Ordered by how strongly the researchers Jey Han Lau trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality (2014) — 650 citations
- Topically Driven Neural Language Model (2017) — 68 citations
- Grammaticality, Acceptability, and Probability: A Probabilistic View of Linguistic Knowledge (2017) — 241 citations
- An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation (2016) — 708 citations
- IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP (2020) — 448 citations
- Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis? (2020) — 44 citations
- Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading Comprehension (2019) — 53 citations
- Novel Word-sense Identification (2014) — 55 citations
- Word Sense Induction for Novel Sense Detection (2012) — 156 citations
- Automatic Evaluation of Topic Coherence (2010) — 1,113 citations