
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games
In visual guessing games, a Guesser has to identify a target object in a...
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Strudel: Learning StructuredDecomposable Probabilistic Circuits
Probabilistic circuits (PCs) represent a probability distribution as a c...
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Handling Missing Data in Decision Trees: A Probabilistic Approach
Decision trees are a popular family of models due to their attractive pr...
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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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Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
Weighted model integration (WMI) is a very appealing framework for proba...
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On Tractable Computation of Expected Predictions
Computing expected predictions has many interesting applications in area...
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Hybrid Probabilistic Inference with Logical Constraints: Tractability and MessagePassing
Weighted model integration (WMI) is a very appealing framework for proba...
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Conditional SumProduct Networks: Imposing Structure on Deep Probabilistic Architectures
Bayesian networks are a central tool in machine learning and artificial ...
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From Variational to Deterministic Autoencoders
Variational Autoencoders (VAEs) provide a theoreticallybacked framework...
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SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using SumProduct Networks
We introduce SPFlow, an opensource Python library providing a simple in...
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Automatic Bayesian Density Analysis
Making sense of a dataset in an automatic and unsupervised fashion is a ...
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Probabilistic Deep Learning using Random SumProduct Networks
Probabilistic deep learning currently receives an increased interest, as...
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SumProduct Networks for Hybrid Domains
While all kinds of mixed data from personal data, over panel and scient...
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Visualizing and Understanding SumProduct Networks
SumProduct Networks (SPNs) are recently introduced deep tractable proba...
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Towards Representation Learning with Tractable Probabilistic Models
Probabilistic models learned as density estimators can be exploited in r...
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Antonio Vergari
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