Deep Learning szeminárium - Gradient presentacions in ReLU networks as similarity functions, the targent sensitivity matrix
A Rényi Intézet Deep Learning szemináriumának következő előadását Rácz Dániel (SZTAKI) tartja március 30-án, szerdán 16:00-korezen
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Absztrakt:
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.