Given a learning task for a data set, learning it together with related tasks (data sets) can improve performance. Gaussian process models have been applied to such multi-task learning scenarios, based on joint priors for functions underlying the tasks. In previous Gaussian process approaches, all tasks have been assumed to be of equal importance, whereas in transfer learning the goal is asymme...

The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea o...

Training generative adversarial networks (GANs) on high quality (HQ) images involves important computing resources. This requirement represents a bottleneck for the development of applications GANs. We propose transfer learning technique GANs that significantly reduces training time. Our approach consists freezing low-level layers both critic and generator original GAN. assume an auto-encoder c...

The question of transfer is a special challenge in mathematics teaching because the wide range and fragmentation curricula have many cases fostered an instrumental understanding, which makes difficult for students. Although promoting relational learning has been huge step forward achieving transfer, understanding usually remains at technical level learning. Fostering critical thinking metacogni...

People learn and induce from prior experiences. We first learn how to use a spoon and then know how to use forks of various sizes. We first learn how to sew and then learn how to embroider. Transferring knowledge from one situation to another related situation often increases the speed and quality of learning. This observation is relevant to human learning, as well as machine learning. This the...