The next presentation of the Rényi Institute's Deep Learning seminar will be held by Rácz Dániel (SZTAKI) at the 30th of March, 16:00 here.
Abstract:
Feed-forward networks can be interpreted as mappings with linear decision surfaces at the level of the last layer. We investigate how the tangent space of the network can be exploited to refine the decision in case of ReLU (Rectified Linear Unit) activations. We show that a simple Riemannian metric parametrized on the parameters of the network forms a similarity function at least as good as the original network and we suggest a sparse metric to increase the similarity gap.