Shijin Wang
Artificial Intelligence · with Cui · 2016–2022
A researcher in the Science corpus: 13 papers; trusts 243 researchers and is trusted by 29 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 220 citations into it; #6,587 of 35,345 by trust here, #4,888 by citations
- Natural Language Processing — 200 citations into it; #1,314 of 5,688 by trust here, #1,121 by citations
- Computer Vision and Pattern Recognition — 103 citations into it; #5,646 of 21,575 by trust here, #4,525 by citations
- Evaluation & Benchmarks — 58 citations into it
- Large Language Models — 39 citations into it; #665 of 799 by trust here, #518 by citations
- Model Architectures — 28 citations into it
- Information Retrieval & Search — 12 citations into it
- Graph Learning — 11 citations into it
Whom Shijin Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yoshua Bengio
- Geoffrey E. Hinton
- Ilya Sutskever
- Alex Krizhevsky
- Jimmy Ba
- Alex Graves
- Oriol Vinyals
- Yann LeCun
- Richard Socher
Who trusts Shijin Wang overall
- Yiming Cui
- Zhuosheng Zhang
- Ting-Wei Liu
- Kai Sun
- Minghao Hu
- Rudolf Kadlec
- Ondrej Bajgar
- Yafang Huang
- Shuohang Wang
- Ziqing Yang
Papers from Shijin Wang's perspective
Ordered by how strongly the researchers Shijin Wang 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
- Gradient-based learning applied to document recognition
- 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)
- 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
- Attention-over-Attention Neural Networks for Reading Comprehension (2016) — 455 citations
- Consensus Attention-based Neural Networks for Chinese Reading Comprehension (2016) — 88 citations
- A Span-Extraction Dataset for Chinese Machine Reading Comprehension (2019) — 214 citations
- Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions (2018) — 31 citations
- Cross-Lingual Machine Reading Comprehension (2019) — 48 citations
- Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network (2022) — 35 citations
- Is Graph Structure Necessary for Multi-hop Question Answering? (2020) — 36 citations
- A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (2020) — 20 citations
- TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing (2020) — 51 citations
- Is Graph Structure Necessary for Multi-hop Reasoning? (2020) — 17 citations
- Revisiting Pre-Trained Models for Chinese Natural Language Processing (2020) — 878 citations
- Pre-Training with Whole Word Masking for Chinese BERT (2019) — 1,316 citations
- Dataset for the First Evaluation on Chinese Machine Reading Comprehension (2018) — 24 citations