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Curriculum Learning
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BibTeX
@MISC{Bengio_curriculumlearning,
author = {Yoshua Bengio and Jérôme Louradour and Ronan Collobert and Jason Weston},
title = {Curriculum Learning},
year = {}
}
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Abstract
Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and gradually more complex ones. Here, we formalize such training strategies in the context of machine learning, and call them “curriculum learning”. In the context of recent research studying the difficulty of training in the presence of non-convex training criteria (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. The experiments show that significant improvements in generalization can be achieved. We hypothesize that curriculum learning has both an effect on the speed of convergence of the training process to a minimum and, in the case of non-convex criteria, on the quality of the local minima obtained: curriculum learning can be seen as a particular form of continuation method (a general strategy for global optimization of non-convex functions). 1.
Keyphrases
curriculum learning particular form non-convex function stochastic neural network training process meaningful order recent research local minimum significant improvement global optimization machine learning training strategy non-convex criterion complex one non-convex training criterion continuation method various set-ups general strategy
