Reviews: A Unified Approach for Learning the Parameters of Sum-Product Networks

The single contribution of the paper which is relevant in practice is an alternative derivation of an existing method (Expectation Maximization for learning SPN weights). While this is an interesting result, I think that it does not grant alone a publication in NIPS since it's hard to imagine how this can contribute to better theoretical understanding or practical applications of SPNs. The interpretation of SPNs as mixtures of tree structured SPNs, which is reported as a novelty by the authors, was actually first derived in [Dennis and Vantura, Greedy Structure Search for Sum-Product Networks, 2015]. The paper is overall well written, clearly structured and the derivation of the results is really interesting. My main concern, as detailed above, is that in my opinion the potential impact of this paper is low, and the novelty is also somewhat limited due to the fact that the interpretation of SPN as mixture of trees was already given in [Dennis and Vantura, Greedy Structure Search for Sum-Product Networks, 2015] and that this is basically just an alternative derivation of EM.