Shu Fan
Electrical and Electronic Engineering · 2006–2016
A researcher in the Science corpus: 8 papers; trusts 25 researchers and is trusted by 58 researchers here, as inferred from the citation graph.
Subfields
- Electrical and Electronic Engineering — 57 citations into it; #292 of 16,539 by trust here, #3,169 by citations
- Management Science and Operations Research — 32 citations into it; #818 of 11,946 by trust here, #1,590 by citations
- Artificial Intelligence — 17 citations into it
- Economics and Econometrics — 10 citations into it; #19,293 of 23,481 by trust here, #9,060 by citations
- Renewable Energy, Sustainability and the Environment — 9 citations into it
- Statistics and Probability — 9 citations into it
- Atmospheric Science — 7 citations into it
- Safety, Risk, Reliability and Quality — 7 citations into it
Whom Shu Fan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Rafal Weron
- Bernard Walter Silverman
- Bo-Juen Chen
- James W. Taylor
- Rob J Hyndman
- Patrick E. McSharry
- Trevor J. Hastie
- Ming-Wei Chang
- Paresh Kumar Narayan
- Sylvain Arlot
Who trusts Shu Fan overall
- Rob J Hyndman
- Tao Hong
- Souhaib Ben Taieb
- Bidong Liu
- Yi Wang
- Rafal Weron
- Han Lin Shang
- Jakub Nowotarski
- Moshoko Emily Lebotsa
- Nathan Charlton
Papers from Shu Fan's perspective
Ordered by how strongly the researchers Shu Fan trusts endorse each paper. Same corpus, a different order for every researcher.
- Regression Shrinkage and Selection via the Lasso
- Generalized Additive Models
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Predicting Time Series with Support Vector Machines
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- A survey of cross-validation procedures for model selection
- Probabilistic forecasts of the magnitude and timing of peak electricity demand (IEEE Transactions on Power Systems)
- Gradient-based learning applied to document recognition
Their papers
- Probabilistic electric load forecasting: A tutorial review (2016) — 1,260 citations
- Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond (2016) — 969 citations
- Density Forecasting for Long-Term Peak Electricity Demand (2010) — 386 citations
- Global Energy Forecasting Competition 2012 (2014) — 555 citations
- Short-Term Load Forecasting Based on a Semi-Parametric Additive Model (2012) — 496 citations
- Short-term load forecasting based on an adaptive hybrid method (2006) — 410 citations
- The price elasticity of electricity demand in South Australia (2011) — 232 citations
- Next-day electricity-price forecasting using a hybrid network (2007) — 124 citations