Curriculum learning
Université de Montréal · Princeton University
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…
Citation impact
- FWCI
- 31.24
- Percentile
- 100%
- References
- 39
Authors
4Topics & keywords
- Curriculum
- Computer science
- Generalization
- Context (archaeology)
- Maxima and minima
- Process (computing)
- Convergence (economics)
- Artificial intelligence
- Quality Education