1 citations · 1 across the 7 of their papers we have counts for
5 papers · 1 filter
Deep regression learning from dependent observations with minimum error entropy principle
William Kengne, Modou Wade
This paper considers nonparametric regression from strongly mixing observations. The proposed approach is based on deep neural networks with minimum error entropy (MEE) principle.…
Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimality
William Kengne, Modou Wade
The explicit regularization and optimality of deep neural networks estimators from independent data have made considerable progress recently. The study of such properties on depend…
Robust deep learning from weakly dependent data
William Kengne, Modou Wade
Recent developments on deep learning established some theoretical properties of deep neural networks estimators. However, most of the existing works on this topic are restricted to…
Penalized deep neural networks estimator with general loss functions under weak dependence
William Kengne, Modou Wade
This paper carries out sparse-penalized deep neural networks predictors for learning weakly dependent processes, with a broad class of loss functions. We deal with a general framew…
Sparse-penalized deep neural networks estimator under weak dependence
William Kengne, Modou Wade
We consider the nonparametric regression and the classification problems for -weakly dependent processes. This weak dependence structure is more general than conditions such as,…