Optimisation of Overparametrized Sum-Product Networks

05/20/2019

∙

by   Martin Trapp, et al.

∙

0

∙

share

It seems to be a pearl of conventional wisdom that parameter learning in deep sum-product networks is surprisingly fast compared to shallow mixture models. This paper examines the effects of overparameterization in sum-product networks on the speed of parameter optimisation. Using theoretical analysis and empirical experiments, we show that deep sum-product networks exhibit an implicit acceleration compared to their shallow counterpart. In fact, gradient-based optimisation in deep sum-product networks is equal to gradient ascend with adaptive and time-varying learning rates and additional momentum terms.