4,264 research outputs found
Variational auto-encoded deep Gaussian processes
We develop a scalable deep non-parametric generative model by augmenting deep
Gaussian processes with a recognition model. Inference is performed in a novel
scalable variational framework where the variational posterior distributions
are reparametrized through a multilayer perceptron. The key aspect of this
reformulation is that it prevents the proliferation of variational parameters
which otherwise grow linearly in proportion to the sample size. We derive a new
formulation of the variational lower bound that allows us to distribute most of
the computation in a way that enables to handle datasets of the size of
mainstream deep learning tasks. We show the efficacy of the method on a variety
of challenges including deep unsupervised learning and deep Bayesian
optimization
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding
Human face exhibits an inherent hierarchy in its representations (i.e.,
holistic facial expressions can be encoded via a set of facial action units
(AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown
great results in unsupervised extraction of hierarchical latent representations
from large amounts of image data, while being robust to noise and other
undesired artifacts. Potentially, this makes VAEs a suitable approach for
learning facial features for AU intensity estimation. Yet, most existing
VAE-based methods apply classifiers learned separately from the encoded
features. By contrast, the non-parametric (probabilistic) approaches, such as
Gaussian Processes (GPs), typically outperform their parametric counterparts,
but cannot deal easily with large amounts of data. To this end, we propose a
novel VAE semi-parametric modeling framework, named DeepCoder, which combines
the modeling power of parametric (convolutional) and nonparametric (ordinal
GPs) VAEs, for joint learning of (1) latent representations at multiple levels
in a task hierarchy1, and (2) classification of multiple ordinal outputs. We
show on benchmark datasets for AU intensity estimation that the proposed
DeepCoder outperforms the state-of-the-art approaches, and related VAEs and
deep learning models.Comment: ICCV 2017 - accepte
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
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