Xiaosheng Si
Artificial Intelligence · 2011–2019
A researcher in the Science corpus: 3 papers; trusts 9 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 27 citations into it; #31,463 of 35,345 by trust here, #21,878 by citations
- Safety, Risk, Reliability and Quality — 27 citations into it; #350 of 1,220 by trust here, #67 by citations
- Statistics and Probability — 21 citations into it; #7,484 of 12,932 by trust here, #2,023 by citations
- Model Architectures — 19 citations into it
- Control and Systems Engineering — 15 citations into it
- Automotive Engineering — 10 citations into it; #2,986 of 3,081 by trust here, #2,035 by citations
- Signal Processing — 9 citations into it; #6,782 of 7,572 by trust here, #4,051 by citations
- Natural Language Processing — 6 citations into it
Whom Xiaosheng Si trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Lawrence R. Rabiner
- Hongzhou Wang
- Jan M. van Noortwijk
- Shunzheng Yu
- O. Aalen
- Nikhil M. Vichare
- Michael G. Pecht
- Hai Qiu
- Jeffrey P. Kharoufeh
- Pradeep Lall
Who trusts Xiaosheng Si overall
- Xiaolin Wang
- Mohammad Hadigol
- Dong Wang
- Lifeng Wu
- Yaguo Lei
- Paulo da Costa
- Shuai Zheng
- Phuc Do
- Shengjing Tang
- Xiaohui Si
Papers from Xiaosheng Si's perspective
Ordered by how strongly the researchers Xiaosheng Si 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)
- Gradient-based learning applied to document 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)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Going deeper with convolutions
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Remaining useful life estimation - A review on the statistical data driven approaches (2011) — 2,054 citations
- A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures (2019) — 5,068 citations
- Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error (2014) — 246 citations