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Poster
Concrete Autoencoders: Differentiable Feature Selection and Reconstruction
Muhammed Fatih Balın · Abubakar Abid · James Zou

Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #188
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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 used 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, and the code for the algorithm and experiments is publicly available.

Keywords: Architectures · Dimensionality Reduction · Unsupervised Learning

Author Information

Muhammed Fatih Balın (Bogazici )
Abubakar Abid (Stanford)
James Zou (Stanford University)

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