Neural Networks

There is a growing trend to teach playing an instrument such as

Learning to play piano is a highly demanding activity, which is characterized by a high mental load. To reduce the mental load and, by doing so, to accelerate the learning process, we pursue to use exoskeleton Dexmo.

In the last decades, many researchers have used evolutionary algorithms to adapt the topology and connection weights of recurrent neural networks for various control tasks. This has become a useful machine learning technique. Because handling large genomes is difficult, however, these neural networks typically contain only a few neurons. If the genome contains a recipe for construction of the network instead of the network itself, it can be much smaller. We have developed a method than can exactly do this, and performs very well on a number of different problems.

Unsupervised Kernel Regression is a recent approach to learning non-linear continuous manifold representations. It has been introduced as unsupervised counterpart of the Nadaraya-Watson kernel regression estimator and uses this estimator to find both a latent space representation of a dataset and a smooth mapping from latent space back to the space of the original data. UKR has two main advantages: (1) one can apply leave-one-out cross-validation as an automatic complexity control without additional computational cost and (2) it requires only very few a priori specifications of parameters.