9,983 research outputs found
Grammar Variational Autoencoder
Deep generative models have been wildly successful at learning coherent
latent representations for continuous data such as video and audio. However,
generative modeling of discrete data such as arithmetic expressions and
molecular structures still poses significant challenges. Crucially,
state-of-the-art methods often produce outputs that are not valid. We make the
key observation that frequently, discrete data can be represented as a parse
tree from a context-free grammar. We propose a variational autoencoder which
encodes and decodes directly to and from these parse trees, ensuring the
generated outputs are always valid. Surprisingly, we show that not only does
our model more often generate valid outputs, it also learns a more coherent
latent space in which nearby points decode to similar discrete outputs. We
demonstrate the effectiveness of our learned models by showing their improved
performance in Bayesian optimization for symbolic regression and molecular
synthesis
Leveraging Variational Autoencoders for Parameterized MMSE Channel Estimation
In this manuscript, we propose to utilize the generative neural network-based
variational autoencoder for channel estimation. The variational autoencoder
models the underlying true but unknown channel distribution as a conditional
Gaussian distribution in a novel way. The derived channel estimator exploits
the internal structure of the variational autoencoder to parameterize an
approximation of the mean squared error optimal estimator resulting from the
conditional Gaussian channel models. We provide a rigorous analysis under which
conditions a variational autoencoder-based estimator is mean squared error
optimal. We then present considerations that make the variational
autoencoder-based estimator practical and propose three different estimator
variants that differ in their access to channel knowledge during the training
and evaluation phase. In particular, the proposed estimator variant trained
solely on noisy pilot observations is particularly noteworthy as it does not
require access to noise-free, ground-truth channel data during training or
evaluation. Extensive numerical simulations first analyze the internal behavior
of the variational autoencoder-based estimators and then demonstrate excellent
channel estimation performance compared to related classical and machine
learning-based state-of-the-art channel estimators.Comment: 13 pages, 12 figure
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