You are all cordially invited to the AMLab seminar at Tuesday June 28 at 16:00 in C3.163, where Tameem Adel will give a talk titled “Collapsed Variational Inference for Sum-Product Networks”. Afterwards there are the usual drinks!
Abstract: Sum-Product Networks (SPNs) are probabilistic inference machines that admit exact inference in linear time in the size of the network. Existing parameter learning approaches for SPNs are largely based on the maximum likelihood principle and hence are subject to overfitting compared to more Bayesian approaches. Exact Bayesian posterior inference for SPNs is computationally intractable. We recently proposed a novel deterministic collapsed variational inference algorithm for SPNs that is computationally efficient, easy to implement and at the same time allows us to incorporate prior information into the optimization formulation. Experiments show a significant improvement in accuracy compared with a maximum likelihood based approach.