184,431 research outputs found
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
Probabilistic graphical models are a central tool in AI; however, they are
generally not as expressive as deep neural models, and inference is notoriously
hard and slow. In contrast, deep probabilistic models such as sum-product
networks (SPNs) capture joint distributions in a tractable fashion, but still
lack the expressive power of intractable models based on deep neural networks.
Therefore, we introduce conditional SPNs (CSPNs), conditional density
estimators for multivariate and potentially hybrid domains which allow
harnessing the expressive power of neural networks while still maintaining
tractability guarantees. One way to implement CSPNs is to use an existing SPN
structure and condition its parameters on the input, e.g., via a deep neural
network. This approach, however, might misrepresent the conditional
independence structure present in data. Consequently, we also develop a
structure-learning approach that derives both the structure and parameters of
CSPNs from data. Our experimental evidence demonstrates that CSPNs are
competitive with other probabilistic models and yield superior performance on
multilabel image classification compared to mean field and mixture density
networks. Furthermore, they can successfully be employed as building blocks for
structured probabilistic models, such as autoregressive image models.Comment: 13 pages, 6 figure
Learning Credal Sum-Product Networks
Probabilistic representations, such as Bayesian and Markov networks, are
fundamental to much of statistical machine learning. Thus, learning
probabilistic representations directly from data is a deep challenge, the main
computational bottleneck being inference that is intractable. Tractable
learning is a powerful new paradigm that attempts to learn distributions that
support efficient probabilistic querying. By leveraging local structure,
representations such as sum-product networks (SPNs) can capture high tree-width
models with many hidden layers, essentially a deep architecture, while still
admitting a range of probabilistic queries to be computable in time polynomial
in the network size. While the progress is impressive, numerous data sources
are incomplete, and in the presence of missing data, structure learning methods
nonetheless revert to single distributions without characterizing the loss in
confidence. In recent work, credal sum-product networks, an imprecise extension
of sum-product networks, were proposed to capture this robustness angle. In
this work, we are interested in how such representations can be learnt and thus
study how the computational machinery underlying tractable learning and
inference can be generalized for imprecise probabilities.Comment: Accepted to AKBC 202
Probabilistic Relational Model Benchmark Generation
The validation of any database mining methodology goes through an evaluation
process where benchmarks availability is essential. In this paper, we aim to
randomly generate relational database benchmarks that allow to check
probabilistic dependencies among the attributes. We are particularly interested
in Probabilistic Relational Models (PRMs), which extend Bayesian Networks (BNs)
to a relational data mining context and enable effective and robust reasoning
over relational data. Even though a panoply of works have focused, separately ,
on the generation of random Bayesian networks and relational databases, no work
has been identified for PRMs on that track. This paper provides an algorithmic
approach for generating random PRMs from scratch to fill this gap. The proposed
method allows to generate PRMs as well as synthetic relational data from a
randomly generated relational schema and a random set of probabilistic
dependencies. This can be of interest not only for machine learning researchers
to evaluate their proposals in a common framework, but also for databases
designers to evaluate the effectiveness of the components of a database
management system
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