Current Research

You can find our current research activities here. For the bigger picture, be sure to check out our Research Roadmap and approach to AGI.


  • We are finishing up a series of experiments comparing sparse unsupervised image classification, focusing on the MNIST dataset. In particular we have covered Sparse Autoencoders and Convolutional-Growing-Neural-Gas (representing a broader family of Convolutional-Competitive-Learning methods). The latter has the added benefit that it works equally well on nonstationary input.
  • We plan to work with more sophisticated datasets, starting with Street-View-House-Numbers (SVHN).
  • We are actively investigating whether Capsules Networks can be trained in an unsupervised manner.

Theory & Literature Review