Wanxiang Che
Artificial Intelligence · 2010–2021
A researcher in the Science corpus: 21 papers; trusts 535 researchers and is trusted by 28 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 461 citations into it; #8,486 of 35,345 by trust here, #4,353 by citations
- Natural Language Processing — 374 citations into it; #3,021 of 5,688 by trust here, #1,674 by citations
- Computer Vision and Pattern Recognition — 201 citations into it; #15,636 of 21,575 by trust here, #6,206 by citations
- Evaluation & Benchmarks — 81 citations into it; #2,476 of 3,147 by trust here, #2,903 by citations
- Representation & Self-Supervised Learning — 58 citations into it
- Large Language Models — 52 citations into it; #664 of 799 by trust here, #520 by citations
- Model Architectures — 34 citations into it
- Safety, Robustness & Interpretability — 27 citations into it
Whom Wanxiang Che trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Jimmy Ba
- Sepp Hochreiter
- Yann LeCun
- Richard Socher
- Kaiming He
Who trusts Wanxiang Che overall
- Yiming Cui
- Ting-Wei Liu
- Xuanwei Zhang
- Shiliang Sun
- Ziqing Yang
- Shijin Wang
- Bing Qin
- Qianqian Dong
- Xiao Xu
- Xiaocheng Feng
Papers from Wanxiang Che's perspective
Ordered by how strongly the researchers Wanxiang Che trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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
- Learning Semantic Hierarchies via Word Embeddings (2014) — 290 citations
- Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting (2020) — 270 citations
- LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2020) — 702 citations
- Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency (2019) — 822 citations
- NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation (2021) — 104 citations
- Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network (2020) — 207 citations
- Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog (2020) — 96 citations
- Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding (2018) — 160 citations
- A Span-Extraction Dataset for Chinese Machine Reading Comprehension (2019) — 214 citations
- Cross-Lingual Machine Reading Comprehension (2019) — 48 citations
- Data Augmentation Approaches in Natural Language Processing: A Survey (2021) — 386 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
- Revisiting Pre-Trained Models for Chinese Natural Language Processing (2020) — 878 citations
- Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network (2020) — 28 citations
- Pre-Training with Whole Word Masking for Chinese BERT (2019) — 1,316 citations
- A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding (2019) — 295 citations
- Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning (2016) — 21 citations
- A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks (2016) — 33 citations
- Convolution Neural Network for Relation Extraction (2013) — 172 citations