Cornelis W. Oosterlee

Finance · 2003–2018

A researcher in the Science corpus: 14 papers; trusts 181 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Finance — 77 citations into it; #2,037 of 10,518 by trust here, #3,667 by citations
• Artificial Intelligence — 30 citations into it; #27,846 of 35,345 by trust here, #33,294 by citations
• Economics and Econometrics — 29 citations into it; #5,638 of 23,481 by trust here, #10,872 by citations
• Computer Vision and Pattern Recognition — 22 citations into it
• Management Science and Operations Research — 21 citations into it; #2,193 of 11,946 by trust here, #3,357 by citations
• Model Architectures — 19 citations into it; #781 of 2,278 by trust here, #1,601 by citations
• Demography — 16 citations into it; #2,520 of 4,236 by trust here, #4,149 by citations
• Signal Processing — 14 citations into it; #1,577 of 7,572 by trust here, #2,816 by citations

Whom Cornelis W. Oosterlee trusts overall

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

1. Leif Andersen
2. René L. Schilling
3. Steven Kou
4. Rama Cont
5. Peter P. Carr
6. Diederik P. Kingma
7. Fang Fang
8. Kaiming He
9. Dilip B Madan 5
10. Alexander J. McNeil

Who trusts Cornelis W. Oosterlee overall

1. Samuli Ikonen
2. Jari Toivanen
3. Hans De Sterck
4. Tinne Haentjens
5. Michel H. Vellekoop
6. Carl Chiarella
7. H. Nieuwenhuis
8. K. J. in ’t Hout
9. Eduardo Abi Jaber
10. Santtu Salmi

Papers from Cornelis W. Oosterlee's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Conditional Time Series Forecasting with Convolutional Neural Networks (2017) — 591 citations
• A Novel Multigrid Based Preconditioner For Heterogeneous Helmholtz Problems (2005) — 360 citations
• Dilated convolutional neural networks for time series forecasting (2018) — 117 citations
• On a class of preconditioners for solving the Helmholtz equation (2004) — 317 citations
• The Heston Stochastic-Local Volatility Model: Efficient Monte Carlo Simulation (2014) — 71 citations
• Extension of stochastic volatility equity models with the Hull–White interest rate process (2012) — 74 citations
• A LOW-BIAS SIMULATION SCHEME FOR THE SABR STOCHASTIC VOLATILITY MODEL (2012) — 48 citations
• On Cross-Currency Models with Stochastic Volatility and Correlated Interest Rates (2010) — 60 citations
• Pricing early-exercise and discrete barrier options by fourier-cosine series expansions (2009) — 302 citations
• A Novel Pricing Method for European Options Based on Fourier-Cosine Series Expansions (2008) — 733 citations
• Generalized beta regression models for random loss given default (2008) — 59 citations
• On American Options Under the Variance Gamma Process (2007) — 74 citations
• Numerical valuation of options with jumps in the underlying (2005) — 170 citations
• On multigrid for linear complementarity problems with application to American-style options. (2003) — 148 citations