Curriculum Learning | Bengio et al. | ICML 2009 | 2009

Brief information Authors: Yoshua Bengio, Jérôme Louradour, Ronan Collobert, Jason Weston Published year: 2009 Publication: ICML 2009 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. We formalize such training strategies in the context of […]

One-Shot Imitation Learning | Yan Duan et al. | 2017

Summary Abstract Ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering. In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning. Task examples: to stack all blocks […]

Conditional Generative Adversarial Nets | M. Mirza, S. Osindero | 2014

Introduction Conditional version of Generative Adversarial Nets (GAN) where both generator and discriminator are conditioned on some data y (class label or data from some other modality). Architecture Feed y into both the generator and discriminator as additional input layers such that y and input are combined in a joint hidden representation.