Jiaqi Guo
Natural Language Processing · 2019–2021
A researcher in the Science corpus: 3 papers; trusts 70 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Natural Language Processing — 82 citations into it; #4,103 of 5,688 by trust here, #2,846 by citations
- Artificial Intelligence — 81 citations into it; #24,567 of 35,345 by trust here, #12,761 by citations
- Computer Vision and Pattern Recognition — 27 citations into it
- Model Architectures — 11 citations into it
- Management Science and Operations Research — 10 citations into it
- Information Extraction — 9 citations into it
Whom Jiaqi Guo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Percy Liang
- Diederik P. Kingma
- Ilya Sutskever
- Yoshua Bengio
- Geoffrey E. Hinton
- Oriol Vinyals
- Luke S. Zettlemoyer
- Sepp Hochreiter
- Alex Krizhevsky
- Dzmitry Bahdanau
Who trusts Jiaqi Guo overall
- Zhuang Li
- Peter Alexander Jansen
- M. Pourreza
- Wenhu Chen
- Lizhen Qu
- Byeong-Hoon So
- Jonathan Herzig
- Qian Liu
- Pengcheng Yin
- Bailin Wang
Papers from Jiaqi Guo's perspective
Ordered by how strongly the researchers Jiaqi Guo trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- 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
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation (2019) — 493 citations
- TAPEX: Table Pre-training via Learning a Neural SQL Executor (2021) — 368 citations
- Benchmarking Meaning Representations in Neural Semantic Parsing (2020) — 29 citations