Ning Dai

Artificial Intelligence · with Huang · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 176 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 206 citations into it; #30,532 of 35,345 by trust here, #6,911 by citations
• Natural Language Processing — 132 citations into it; #4,968 of 5,688 by trust here, #3,687 by citations
• Computer Vision and Pattern Recognition — 83 citations into it; #18,835 of 21,575 by trust here, #4,988 by citations
• Large Language Models — 64 citations into it
• Representation & Self-Supervised Learning — 47 citations into it
• Model Architectures — 25 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Safety, Robustness & Interpretability — 11 citations into it

Whom Ning Dai trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ronald J. Williams
4. Diederik P. Kingma
5. Ilya Sutskever
6. Alex Krizhevsky
7. Alex Graves
8. Alec Radford
9. Yann LeCun
10. Ruslan Salakhutdinov

Who trusts Ning Dai overall

1. Kevin Yang
2. Ruibo Liu
3. Kalpesh Krishna
4. Mirac Suzgun
5. Zhiqiang Hu
6. Akhilesh Sudhakar
7. Xinyao Ma
8. Leo Laugier
9. Ashish Sharma
10. Yuan Li

Papers from Ning Dai's perspective

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

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

• Pre-trained models for natural language processing: A survey (2020) — 1,716 citations
• Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation (2019) — 221 citations