Qingxiu Dong
Artificial Intelligence · 2022–2024
A researcher in the Science corpus: 4 papers; trusts 43 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 181 citations into it
- Prompting & Reasoning — 65 citations into it
- Computer Vision and Pattern Recognition — 54 citations into it
- Natural Language Processing — 48 citations into it
- Model Architectures — 8 citations into it
- Computer Networks and Communications — 6 citations into it
Whom Qingxiu Dong trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Sewon Min
- Diederik P. Kingma
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Yann LeCun
- Alex Graves
- Jimmy Ba
Who trusts Qingxiu Dong overall
Papers from Qingxiu Dong's perspective
Ordered by how strongly the researchers Qingxiu Dong 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
- A Survey on In-context Learning (2022) — 1,101 citations
- Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding (2024) — 297 citations
- A Survey for In-context Learning (2023) — 401 citations
- Can We Edit Factual Knowledge by In-Context Learning? (2023) — 356 citations