Jingbo Shang

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 5 papers; trusts 116 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #21,373 of 35,345 by trust here, #13,544 by citations
• Natural Language Processing — 70 citations into it; #3,902 of 5,688 by trust here, #3,190 by citations
• Computer Vision and Pattern Recognition — 43 citations into it
• Information Systems — 19 citations into it
• Representation & Self-Supervised Learning — 19 citations into it; #2,293 of 3,145 by trust here, #2,148 by citations
• Evaluation & Benchmarks — 17 citations into it
• Large Language Models — 17 citations into it
• Management Science and Operations Research — 10 citations into it

Whom Jingbo Shang trusts overall

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

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

Who trusts Jingbo Shang overall

1. Liyuan Liu
2. Bill Yuchen Lin
3. Jeremy Howard
4. M. V. Koroteev
5. Jie Yang
6. Jing Li
7. Xusheng Luo
8. Frank F. Xu
9. Xiang Ren
10. Sebastian Ruder

Papers from Jingbo Shang's perspective

Ordered by how strongly the researchers Jingbo Shang 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

• Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling (2018) — 46 citations
• Empower Sequence Labeling with Task-Aware Neural Language Model (2017) — 354 citations
• Text Is All You Need: Learning Language Representations for Sequential Recommendation (2023) — 383 citations
• Leveraging QA Datasets to Improve Generative Data Augmentation (2022) — 21 citations
• Automated Phrase Mining from Massive Text Corpora (2017) — 322 citations