Restricted Boltzmann machines : Performance and behavior on image databases and future plans

par/by Karol Gregor
California Institute of Technology

Inspired by amazing capabilities of cortex and at the same time its relatively homogeneous hierarchical structure, it is a worthwhile pursuit to develop algorithms that can be repeated in a hierarchical fashion and applied to large class of problems including vision, speech recognition and motor control. Restricted Boltzmann machines are a very capable tool and a good step in this direction. First, I will discuss our study of the restricted Boltzmann machines on image databases of Scenes and Caltech 256 as applied to bag of words of sift features. I will show dependencies on various parameters and comparison of performance to other methods. There are other interesting conclusions too : For a given amount of labeled data, the performance improves if the system is pre-trained on a larger number of images ; Neurons seem to appear after unsupervised pre-training that explicitly represent certain categories (to some extent). Next I will discuss a simple experiment with temporal sequences. Then I will put this problem into the context of cortical computations and outline a plan that I believe is very reasonable to undertake in the future.