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Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations
In this work, we propose Sum-Product-Transform 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: Stan-like Speed for Dynamic Probabilistic Models
We present the preliminary high-level design and features of DynamicPPL....
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Deep Structured Mixtures of Gaussian Processes
Gaussian Processes (GPs) are powerful non-parametric Bayesian regression...
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Bayesian Learning of Sum-Product Networks
Sum-product networks (SPNs) are flexible density estimators and have rec...
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Optimisation of Overparametrized Sum-Product 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 Sum-Product Networks
While Gaussian processes (GPs) are the method of choice for regression t...
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Probabilistic Deep Learning using Random Sum-Product Networks
Probabilistic deep learning currently receives an increased interest, as...
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Safe Semi-Supervised Learning of Sum-Product Networks
In several domains obtaining class annotations is expensive while at the...
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