Xiang Dai

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 3 papers; trusts 78 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #18,123 of 35,345 by trust here, #24,417 by citations
• Natural Language Processing — 40 citations into it; #3,153 of 5,688 by trust here, #4,879 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Model Architectures — 13 citations into it
• Large Language Models — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Molecular Biology — 7 citations into it

Whom Xiang Dai trusts overall

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

1. Geoffrey E. Hinton
2. Alex Graves
3. Yann LeCun
4. Yoshua Bengio
5. Ilya Sutskever
6. Diederik P. Kingma
7. Jacob Devlin
8. Alex Krizhevsky
9. Iz Beltagy
10. Quoc V. Le

Who trusts Xiang Dai overall

1. Freda Shi
2. Sufeng Duan
3. Kelechi Ogueji
4. Karen Livescu
5. Hai Zhao
6. Orevaoghene Ahia
7. Kaustubh D. Dhole
8. Kevin Gimpel
9. Maciej A. Mazurowski
10. Sebastian Gehrmann

Papers from Xiang Dai's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Attention is All you Need (Neural Information Processing Systems)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
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. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• An Analysis of Simple Data Augmentation for Named Entity Recognition (2020) — 234 citations
• Revisiting Transformer-based Models for Long Document Classification (2022) — 102 citations
• Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods (2017) — 64 citations