
SumProductTransform Networks: Exploiting Symmetries using Invertible Transformations
In this work, we propose SumProductTransform Networks (SPTN), an exten...
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Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Probabilistic circuits (PCs) are a promising avenue for probabilistic mo...
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DynamicPPL: Stanlike Speed for Dynamic Probabilistic Models
We present the preliminary highlevel design and features of DynamicPPL....
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Deep Structured Mixtures of Gaussian Processes
Gaussian Processes (GPs) are powerful nonparametric Bayesian regression...
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Bayesian Learning of SumProduct Networks
Sumproduct networks (SPNs) are flexible density estimators and have rec...
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Optimisation of Overparametrized SumProduct Networks
It seems to be a pearl of conventional wisdom that parameter learning in...
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Learning Deep Mixtures of Gaussian Process Experts Using SumProduct Networks
While Gaussian processes (GPs) are the method of choice for regression t...
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Probabilistic Deep Learning using Random SumProduct Networks
Probabilistic deep learning currently receives an increased interest, as...
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Safe SemiSupervised Learning of SumProduct Networks
In several domains obtaining class annotations is expensive while at the...
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Martin Trapp
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