376 citations · 1.1k across the 17 of their papers we have counts for
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cs.LG2016★ 8 cited
The Sum-Product Theorem: A Foundation for Learning Tractable Models
Abram L. Friesen, Pedro Domingos
Inference in expressive probabilistic models is generally intractable, which makes them difficult to learn and limits their applicability. Sum-product networks are a class of deep…
cs.AI2016★ 26 cited
Recursive Decomposition for Nonconvex Optimization
Abram L. Friesen, Pedro Domingos
Continuous optimization is an important problem in many areas of AI, including vision, robotics, probabilistic inference, and machine learning. Unfortunately, most real-world optim…
cs.AI2016
On the Latent Variable Interpretation in Sum-Product Networks
Robert Peharz, Robert Gens, Franz Pernkopf +1
One of the central themes in Sum-Product networks (SPNs) is the interpretation of sum nodes as marginalized latent variables (LVs). This interpretation yields an increased syntacti…