Kai Wang

Artificial Intelligence · with Wang · 2019–2023

A researcher in the Science corpus: 3 papers; trusts 84 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it; #25,781 of 35,345 by trust here, #22,463 by citations
• Computer Vision and Pattern Recognition — 48 citations into it
• Natural Language Processing — 35 citations into it; #4,253 of 5,688 by trust here, #5,092 by citations
• Generative Models — 26 citations into it
• Model Architectures — 13 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Signal Processing — 5 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Kai Wang trusts overall

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

1. Diederik P. Kingma
2. Christopher D. Manning
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Jimmy Ba
6. Ilya Sutskever
7. Alex Krizhevsky
8. Yann LeCun
9. Kaiming He
10. Tomas Mikolov

Who trusts Kai Wang overall

1. Nabil Hossain
2. Di Jin
3. Zeqiu Wu
4. Haoyang Li
5. W. Yu
6. Ruifan Li
7. Marjan Ghazvininejad
8. Michel Galley
9. Zhiting Hu
10. Zhijing Jin

Papers from Kai Wang's perspective

Ordered by how strongly the researchers Kai Wang 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization (2019) — 60 citations
• Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models (2023) — 339 citations
• Relational Graph Attention Network for Aspect-based Sentiment Analysis (2020) — 698 citations