Shengxian Wan
Artificial Intelligence · 2015–2016
A researcher in the Science corpus: 4 papers; trusts 43 researchers and is trusted by 25 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 51 citations into it; #8,307 of 35,345 by trust here, #8,893 by citations
- Natural Language Processing — 47 citations into it; #1,770 of 5,688 by trust here, #1,975 by citations
- Computer Vision and Pattern Recognition — 21 citations into it; #6,177 of 21,575 by trust here, #6,326 by citations
- Information Systems — 19 citations into it; #8,050 of 8,771 by trust here, #3,640 by citations
- Model Architectures — 16 citations into it
- Information Retrieval & Search — 13 citations into it
- Representation & Self-Supervised Learning — 11 citations into it
- Optimization & Training — 8 citations into it
Whom Shengxian Wan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Richard Socher
- Alex Graves
- Sepp Hochreiter
- Tomas Mikolov
- Hang Li
- Ilya Sutskever
- Wenpeng Yin
- Dzmitry Bahdanau
Who trusts Shengxian Wan overall
- Jiafeng Guo
- Qian Chen
- Yixing Fan
- Liu Yang
- Wen Wang
- Shervin Minaee
- Yilin Niu
- Chongming Gao
- Wei Yang
- Jun Xu
Papers from Shengxian Wan's perspective
Ordered by how strongly the researchers Shengxian Wan 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)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations (2015) — 359 citations
- Text Matching as Image Recognition (2016) — 578 citations
- Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN (2016) — 176 citations
- Learning Hierarchical Representation Model for NextBasket Recommendation (2015) — 436 citations