On Nonparametric Guidance for Learning Autoencoder Representations

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On Nonparametric Guidance for Learning Autoencoder Representations

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dc.contributor.author Adams, Ryan Prescott
dc.contributor.author Snoek, Jasper
dc.contributor.author Larochelle, Hugo
dc.date.accessioned 2012-05-10T13:44:00Z
dc.date.issued 2012
dc.identifier.citation Snoek, Jasper, Ryan P. Adams, and Hugo Larochelle. 2012. On nonparametric guidance for learning autoencoder representations. In Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics: April 21-23, 2012 La Palma, Canary Islands, ed. Neil Lawrence and Mark Girolami, JMLR Workshop and Conference Proceedings 22:1073-1080. en_US
dc.identifier.issn 1938-7228 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:8712190
dc.description.abstract Unsupervised discovery of latent representations, in addition to being useful for density modeling, visualisation and exploratory data analysis, is also increasingly important for learning features relevant to discriminative tasks. Autoencoders, in particular, have proven to be an effective way to learn latent codes that reflect meaningful variations in data. A continuing challenge, however, is guiding an autoencoder toward representations that are useful for particular tasks. A complementary challenge is to find codes that are invariant to irrelevant transformations of the data. The most common way of introducing such problem-specific guidance in autoencoders has been through the incorporation of a parametric component that ties the latent representation to the label information. In this work, we argue that a preferable approach relies instead on a nonparametric guidance mechanism. Conceptually, it ensures that there exists a function that can predict the label information, without explicitly instantiating that function. The superiority of this guidance mechanism is con- firmed on two datasets. In particular, this approach is able to incorporate invariance information (lighting, elevation, etc.) from the small NORB object recognition dataset and yields state-of-the-art performance for a single layer, non-convolutional network. en_US
dc.description.sponsorship Engineering and Applied Sciences en_US
dc.language.iso en_US en_US
dc.publisher Microtome Publishing en_US
dc.relation.isversionof http://jmlr.csail.mit.edu/proceedings/papers/v22/snoek12/snoek12.pdf en_US
dash.license OAP
dc.title On Nonparametric Guidance for Learning Autoencoder Representations en_US
dc.type Journal Article en_US
dc.description.version Accepted Manuscript en_US
dc.relation.journal Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics en_US
dash.depositing.author Adams, Ryan Prescott
dc.date.available 2012-05-10T13:44:00Z

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  • FAS Scholarly Articles [7594]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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