  We introduce the concrete autoencoder, an end-to-end differentiable method
for global feature selection, which efficiently identifies a subset of the most
informative features and simultaneously learns a neural network to reconstruct
the input data from the selected features. Our method is unsupervised, and is
based on using a concrete selector layer as the encoder and using a standard
neural network as the decoder. During the training phase, the temperature of
the concrete selector layer is gradually decreased, which encourages a
user-specified number of discrete features to be learned. During test time, the
selected features can be used with the decoder network to reconstruct the
remaining input features. We evaluate concrete autoencoders on a variety of
datasets, where they significantly outperform state-of-the-art methods for
feature selection and data reconstruction. In particular, on a large-scale gene
expression dataset, the concrete autoencoder selects a small subset of genes
whose expression levels can be use to impute the expression levels of the
remaining genes. In doing so, it improves on the current widely-used
expert-curated L1000 landmark genes, potentially reducing measurement costs by
20%. The concrete autoencoder can be implemented by adding just a few lines of
code to a standard autoencoder.
