Chenglei Si
Artificial Intelligence · 2020–2022
A researcher in the Science corpus: 4 papers; trusts 47 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 270 citations into it
- Natural Language Processing — 170 citations into it
- Computer Vision and Pattern Recognition — 69 citations into it
Whom Chenglei Si trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Alex Krizhevsky
- Ilya Sutskever
- Richard Socher
- Yann LeCun
- Kaiming He
- Alex Graves
- Jacob Devlin
Who trusts Chenglei Si overall
Papers from Chenglei Si's perspective
Ordered by how strongly the researchers Chenglei Si trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Prompting GPT-3 To Be Reliable (2022) — 378 citations
- Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP (2021) — 222 citations
- BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
- Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning (2020) — 37 citations