Nicolas Huck
Management Science and Operations Research · 2009–2017
A researcher in the Science corpus: 3 papers; trusts 29 researchers and is trusted by 26 researchers here, as inferred from the citation graph.
Subfields
- Management Science and Operations Research — 25 citations into it; #748 of 11,946 by trust here, #2,355 by citations
- Economics and Econometrics — 19 citations into it; #3,794 of 23,481 by trust here, #9,444 by citations
- Finance — 17 citations into it; #1,348 of 10,518 by trust here, #3,438 by citations
- Artificial Intelligence — 13 citations into it
- Statistics and Probability — 9 citations into it
- Model Architectures — 8 citations into it; #1,146 of 2,278 by trust here, #1,665 by citations
- Control and Systems Engineering — 7 citations into it
- Economics & Operations Research — 5 citations into it; #42 of 244 by trust here, #127 by citations
Whom Nicolas Huck trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ralph E. Steuer
- Greg Richards
- Allan G. Timmermann
- Meryem Duygun Fethi
- Salvatore Greco
- Jeffrey L. Elman
- Richard P. Lippmann
- Jyrki Wallenius
- W. Goetzmann
- Fotios Pasiouras
Who trusts Nicolas Huck overall
- Christopher Krauss
- Thomas G. Fischer
- Kannan Govindan
- Sima Siami‐Namini
- Martin Brandt Jepsen
- Yu-Long Zhou
- Georgios Sermpinis
- Alexander Deinert
- Xuan Anh Do
- Neda Tavakoli
Papers from Nicolas Huck's perspective
Ordered by how strongly the researchers Nicolas Huck trusts endorse each paper. Same corpus, a different order for every researcher.
- Dropout: a simple way to prevent neural networks from overfitting
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Decisions with Multiple Objectives: Preferences and Value Tradeoffs
- Gradient-based learning applied to document recognition
- Maxout Networks
- Mastering the game of Go with deep neural networks and tree search
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Learning
- Random Forests
Their papers
- Pairs selection and outranking: An application to the S&P 100 index (2009) — 145 citations
- Pairs trading and outranking: The multi-step-ahead forecasting case (2010) — 135 citations
- Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 (2017) — 670 citations