Lean Yu

Economics and Econometrics · 2008–2020

A researcher in the Science corpus: 12 papers; trusts 47 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Economics and Econometrics — 127 citations into it; #5,502 of 23,481 by trust here, #2,015 by citations
• Renewable Energy, Sustainability and the Environment — 46 citations into it
• Finance — 34 citations into it
• Artificial Intelligence — 32 citations into it; #28,155 of 35,345 by trust here, #25,809 by citations
• Management Science and Operations Research — 27 citations into it; #5,400 of 11,946 by trust here, #1,440 by citations
• Electrical and Electronic Engineering — 20 citations into it; #7,723 of 16,539 by trust here, #8,489 by citations
• Control and Systems Engineering — 17 citations into it; #4,073 of 8,257 by trust here, #2,147 by citations
• Environmental Engineering — 14 citations into it

Whom Lean Yu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Vladimir Naumovich Vapni
2. Vladimir Naumovich Vapnik
3. Leo Breiman
4. Johan A. K. Suykens
5. Kin Keung Lai
6. Kurt Hornik
7. R. Fisher
8. Robert E. Schapire
9. Shouyang Wang
10. David Hilton Wolpert

Who trusts Lean Yu overall

1. Bangzhu Zhu
2. Kaijian He
3. Melike Bildirici
4. A. Ozbayoglu
5. K. Tran
6. Hooman Abdollahi
7. Zhen-hai Guo
8. Dong Han
9. Ling Tang
10. Omer Berat Sezer

Papers from Lean Yu's perspective

Ordered by how strongly the researchers Lean Yu trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. The Nature of Statistical Learning Theory
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm (2008) — 709 citations
• Crude oil price analysis and forecasting using wavelet decomposed ensemble model (2012) — 150 citations
• Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method (2009) — 245 citations
• A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting (2015) — 194 citations
• Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach (2015) — 168 citations
• A deep learning ensemble approach for crude oil price forecasting (2017) — 348 citations
• Evolving Least Squares Support Vector Machines for Stock Market Trend Mining (2009) — 228 citations
• Carbon emissions trading scheme exploration in China: A multi-agent-based model (2015) — 232 citations
• Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models (2020) — 145 citations
• Carbon allowance auction design of China's emissions trading scheme: A multi-agent-based approach (2017) — 119 citations
• A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data (2018) — 141 citations
• Credit risk assessment with a multistage neural network ensemble learning approach (2008) — 323 citations