2,531 research outputs found
Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer
We present a study for the generation of events from a physical process with
deep generative models. The simulation of physical processes requires not only
the production of physical events, but also to ensure these events occur with
the correct frequencies. We investigate the feasibility of learning the event
generation and the frequency of occurrence with Generative Adversarial Networks
(GANs) and Variational Autoencoders (VAEs) to produce events like Monte Carlo
generators. We study three processes: a simple two-body decay, the processes
and including the decay of the top
quarks and a simulation of the detector response. We find that the tested GAN
architectures and the standard VAE are not able to learn the distributions
precisely. By buffering density information of encoded Monte Carlo events given
the encoder of a VAE we are able to construct a prior for the sampling of new
events from the decoder that yields distributions that are in very good
agreement with real Monte Carlo events and are generated several orders of
magnitude faster. Applications of this work include generic density estimation
and sampling, targeted event generation via a principal component analysis of
encoded ground truth data, anomaly detection and more efficient importance
sampling, e.g. for the phase space integration of matrix elements in quantum
field theories.Comment: 24 pages, 10 figure
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
Factor analysis aims to determine latent factors, or traits, which summarize
a given data set. Inter-battery factor analysis extends this notion to multiple
views of the data. In this paper we show how a nonlinear, nonparametric version
of these models can be recovered through the Gaussian process latent variable
model. This gives us a flexible formalism for multi-view learning where the
latent variables can be used both for exploratory purposes and for learning
representations that enable efficient inference for ambiguous estimation tasks.
Learning is performed in a Bayesian manner through the formulation of a
variational compression scheme which gives a rigorous lower bound on the log
likelihood. Our Bayesian framework provides strong regularization during
training, allowing the structure of the latent space to be determined
efficiently and automatically. We demonstrate this by producing the first (to
our knowledge) published results of learning from dozens of views, even when
data is scarce. We further show experimental results on several different types
of multi-view data sets and for different kinds of tasks, including exploratory
data analysis, generation, ambiguity modelling through latent priors and
classification.Comment: 49 pages including appendi
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