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dc.contributor.authorJanz, Den
dc.contributor.authorVan Der Westhuizen, Jen
dc.contributor.authorPaige, Ben
dc.contributor.authorKusner, MJen
dc.contributor.authorHernández-Lobato, JMen
dc.date.accessioned2020-08-05T23:30:13Z
dc.date.available2020-08-05T23:30:13Z
dc.date.issued2018-01-01en
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/308813
dc.description.abstractDeep generative models have been successfully used to learn representations for high-dimensional discrete spaces by representing discrete objects as sequences and employing powerful sequence-based deep models. Unfortunately, these sequence-based models often produce invalid sequences: sequences which do not represent any underlying discrete structure; invalid sequences hinder the utility of such models. As a step towards solving this problem, we propose to learn a deep recurrent validator model, which can estimate whether a partial sequence can function as the beginning of a full, valid sequence. This validator provides insight as to how individual sequence elements influence the validity of the overall sequence, and can be used to constrain sequence based models to generate valid sequences – and thus faithfully model discrete objects. Our approach is inspired by reinforcement learning, where an oracle which can evaluate validity of complete sequences provides a sparse reward signal. We demonstrate its effectiveness as a generative model of Python 3 source code for mathematical expressions, and in improving the ability of a variational autoencoder trained on SMILES strings to decode valid molecular structures.
dc.rightsAll rights reserved
dc.rights.uri
dc.titleLearning a generative model for validity in complex discrete structuresen
dc.typeConference Object
prism.publicationDate2018en
prism.publicationName6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedingsen
dc.identifier.doi10.17863/CAM.55901
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2018-01-01en
rioxxterms.typeConference Paper/Proceeding/Abstracten


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