Zonghan Yang
Artificial Intelligence · 2022–2023
A researcher in the Science corpus: 2 papers; trusts 146 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 92 citations into it
- Natural Language Processing — 46 citations into it
- Large Language Models — 33 citations into it
- Computer Vision and Pattern Recognition — 30 citations into it
- Safety, Robustness & Interpretability — 19 citations into it
- Prompting & Reasoning — 15 citations into it
- Efficiency & Compression — 13 citations into it
- Representation & Self-Supervised Learning — 8 citations into it
Whom Zonghan Yang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Yann LeCun
- Richard Socher
- Alex Krizhevsky
- Ilya Sutskever
- Jimmy Ba
- Kaiming He
- Takeru Miyato
Who trusts Zonghan Yang overall
Papers from Zonghan Yang's perspective
Ordered by how strongly the researchers Zonghan Yang trusts endorse each paper. Same corpus, a different order for every researcher.
- 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- 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
- On Robust Prefix-Tuning for Text Classification (2022) — 23 citations
- Parameter-efficient fine-tuning of large-scale pre-trained language models (2023) — 1,192 citations