Yue Wang

Artificial Intelligence · with Hoi · 2017–2023

A researcher in the Science corpus: 5 papers; trusts 284 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 149 citations into it; #19,467 of 35,345 by trust here, #6,956 by citations
• Natural Language Processing — 97 citations into it; #2,894 of 5,688 by trust here, #1,857 by citations
• Computer Vision and Pattern Recognition — 82 citations into it
• Information Systems — 71 citations into it; #3,138 of 8,771 by trust here, #750 by citations
• Large Language Models — 46 citations into it
• Software — 46 citations into it
• Code Generation & Program Synthesis — 36 citations into it; #145 of 220 by trust here, #55 by citations
• Multimodal Learning — 36 citations into it

Whom Yue Wang trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Ilya Sutskever
4. Yoshua Bengio
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Yann LeCun
8. Jimmy Ba
9. Alec Radford
10. Oriol Vinyals

Who trusts Yue Wang overall

1. Chun Xia
2. Jiawei Liu
3. Can Xu
4. Baptiste Rozière
5. Yichen Xu
6. Humza Naveed
7. Hung Le
8. Xinyun Chen
9. Ziyang Luo
10. Haipeng Luo

Papers from Yue Wang's perspective

Ordered by how strongly the researchers Yue 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. 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. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• VD-BERT: A Unified Vision and Dialog Transformer with BERT (2020) — 111 citations
• CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation (2021) — 2,385 citations
• CodeT5+: Open Code Large Language Models for Code Understanding and Generation (2023) — 742 citations
• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning (2022) — 502 citations
• Code Completion with Neural Attention and Pointer Networks (2017) — 246 citations