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• W2803766319 abstract "Parameterizing the approximate posterior of a generative model with neural networks has become a common theme in recent machine learning research. While providing appealing flexibility, this approach makes it difficult to impose or assess structural constraints such as conditional independence. We propose a framework for learning representations that relies on Auto-Encoding Variational Bayes and whose search space is constrained via kernel-based measures of independence. In particular, our method employs the $d$-variable Hilbert-Schmidt Independence Criterion (dHSIC) to enforce independence between the latent representations and arbitrary nuisance factors. We show how to apply this method to a range of problems, including the problems of learning invariant representations and the learning of interpretable representations. We also present a full-fledged application to single-cell RNA sequencing (scRNA-seq). In this setting the biological signal is mixed in complex ways with sequencing errors and sampling effects. We show that our method out-performs the state-of-the-art in this domain." @default.
• W2803766319 created "2018-06-01" @default.
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• W2803766319 date "2018-05-22" @default.
• W2803766319 modified "2023-09-23" @default.
• W2803766319 title "Information Constraints on Auto-Encoding Variational Bayes" @default.
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• W2803766319 hasPublicationYear "2018" @default.
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