Shouxiang Wang
Applied Energy · 2013–2020
A researcher in the Science corpus: 5 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.
Whom Shouxiang Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Reuven Y. Rubinstein
- J. Urgen Schmidhuber
- Sepp Hochreiter
- Geoffrey E. Hinton
- Ilya Sutskever
- Yann LeCun
- Yoshua Bengio
- Kaiming He
- Alex Krizhevsky
Who trusts Shouxiang Wang overall
Papers from Shouxiang Wang's perspective
Ordered by how strongly the researchers Shouxiang Wang 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)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- New Metrics for Assessing the Reliability and Economics of Microgrids in Distribution System (2013) — 181 citations
- A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network (2019) — 357 citations
- Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method (2016) — 615 citations
- Interval optimization for integrated electrical and natural-gas systems with power to gas considering uncertainties (2020) — 51 citations
- Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting (2019) — 318 citations