Composite quantile regression neural network with applications
By Qifa Xu, Kai Deng, Cuixia Jiang, Fang Sun, Xue Huang. 2017
98 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jan Gasthaus
- Bernie Wang
- Tim Januschowski
- David Salinas
- Syama Sundar Rangapuram
- Konstantinos Benidis
- Valentin Flunkert
- Kari Torkkola
In Computer Vision and Pattern Recognition
- Jan Gasthaus
- David Salinas
- Kari Torkkola
- Konstantinos Benidis
- Ruofeng Wen
- Valentin Flunkert
- Tim Januschowski
- Syama Sundar Rangapuram
In Statistics and Probability
- David Salinas
- Kari Torkkola
- Konstantinos Benidis
- Ruofeng Wen
- Valentin Flunkert
- Jan Gasthaus
- Tim Januschowski
- Syama Sundar Rangapuram
Citations in the corpus
- Deep Generative Quantile-Copula Models for Probabilistic Forecasting (2019, background): “If the target y is d-dimensional, Wen et al, 2017 and Xu et al, 2017 showed that all of the marginal quantiles can be efficiently predicted by a neural net with matrix output Y (u) d×m = [y (uj) i ]i,j , given the fixed list ofm quantile indices.”
- Probabilistic Forecasting with Spline Quantile Function RNNs (2019, methodology): “Xu et al. (2017) propose a NN that outputs the value of a given quantile and is trained using the pinball loss (and the Huber version of it (Cannon, 2011)) for a grid of di ↵ erent quantiles.”