1,090 research outputs found
Optimal Image Reconstruction in Radio Interferometry
We introduce a method for analyzing radio interferometry data which produces
maps which are optimal in the Bayesian sense of maximum posterior probability
density, given certain prior assumptions. It is similar to maximum entropy
techniques, but with an exact accounting of the multiplicity instead of the
usual approximation involving Stirling's formula. It also incorporates an Occam
factor, automatically limiting the effective amount of detail in the map to
that justified by the data. We use Gibbs sampling to determine, to any desired
degree of accuracy, the multi-dimensional posterior density distribution. From
this we can construct a mean posterior map and other measures of the posterior
density, including confidence limits on any well-defined function of the
posterior map.Comment: 41 pages, 11 figures. High resolution figures 8 and 9 available at
http://www.astro.uiuc.edu/~bwandelt/SuttonWandelt200
Neural Discrete Representation Learning
Learning useful representations without supervision remains a key challenge
in machine learning. In this paper, we propose a simple yet powerful generative
model that learns such discrete representations. Our model, the Vector
Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways:
the encoder network outputs discrete, rather than continuous, codes; and the
prior is learnt rather than static. In order to learn a discrete latent
representation, we incorporate ideas from vector quantisation (VQ). Using the
VQ method allows the model to circumvent issues of "posterior collapse" --
where the latents are ignored when they are paired with a powerful
autoregressive decoder -- typically observed in the VAE framework. Pairing
these representations with an autoregressive prior, the model can generate high
quality images, videos, and speech as well as doing high quality speaker
conversion and unsupervised learning of phonemes, providing further evidence of
the utility of the learnt representations
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