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/Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057)
/Publisher (Curran Associates\054 Inc\056)
/Language (en\055US)
/Created (2018)
/EventType (Poster)
/Description-Abstract (Sum\055product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable\056 Those properties follow from some conditions \050i\056e\056\054 completeness and decomposability\051 that must be respected by the structure of the network\056 As a result\054 it is not easy to specify a valid sum\055product network by hand and therefore structure learning techniques are typically used in practice\056 This paper describes a new online structure learning technique for feed\055forward and recurrent SPNs\056 The algorithm is demonstrated on real\055world datasets with continuous features for which it is not clear what network architecture might be best\054 including sequence datasets of varying length\056)
/Producer (PyPDF2)
/Title (Online Structure Learning for Feed\055Forward and Recurrent Sum\055Product Networks)
/Date (2018)
/ModDate (D\07220190219000753\05508\04700\047)
/Published (2018)
/Type (Conference Proceedings)
/firstpage (6944)
/Book (Advances in Neural Information Processing Systems 31)
/Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051)
/Editors (S\056 Bengio and H\056 Wallach and H\056 Larochelle and K\056 Grauman and N\056 Cesa\055Bianchi and R\056 Garnett)
/Author (Agastya Kalra\054 Abdullah Rashwan\054 Wei\055Shou Hsu\054 Pascal Poupart\054 Prashant Doshi\054 Georgios Trimponias)
/lastpage (6954)
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