Deep Hybrid Models: Bridging Discriminative and Generative Approaches

Volodymyr Kuleshov, Stefano Ermon
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:271-280, 2017.

Abstract

Most methods in machine learning are described as either discriminative or generative. The for- mer often attain higher predictive accuracy, while the latter are more strongly regularized and can deal with missing data. Here, we propose a new framework to combine a broad class of discriminative and generative models, interpo- lating between the two extremes with a multi- conditional likelihood objective. Unlike previ- ous approaches, we couple the two components through shared latent variables, and train using recent advances in variational inference. Instanti- ating our framework with modern deep architec- tures gives rise to deep hybrid models, a highly flexible family that generalizes several existing models and is effective in the semi-supervised setting, where it results in improvements over the state of the art on the SVHN dataset.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-kuleshov17a, title = {Deep Hybrid Models: Bridging Discriminative and Generative Approaches}, author = {Kuleshov, Volodymyr and Ermon, Stefano}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {271--280}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/kuleshov17a/kuleshov17a.pdf}, url = {https://proceedings.mlr.press/r15/kuleshov17a.html}, abstract = {Most methods in machine learning are described as either discriminative or generative. The for- mer often attain higher predictive accuracy, while the latter are more strongly regularized and can deal with missing data. Here, we propose a new framework to combine a broad class of discriminative and generative models, interpo- lating between the two extremes with a multi- conditional likelihood objective. Unlike previ- ous approaches, we couple the two components through shared latent variables, and train using recent advances in variational inference. Instanti- ating our framework with modern deep architec- tures gives rise to deep hybrid models, a highly flexible family that generalizes several existing models and is effective in the semi-supervised setting, where it results in improvements over the state of the art on the SVHN dataset.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Deep Hybrid Models: Bridging Discriminative and Generative Approaches %A Volodymyr Kuleshov %A Stefano Ermon %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-kuleshov17a %I PMLR %P 271--280 %U https://proceedings.mlr.press/r15/kuleshov17a.html %V R15 %X Most methods in machine learning are described as either discriminative or generative. The for- mer often attain higher predictive accuracy, while the latter are more strongly regularized and can deal with missing data. Here, we propose a new framework to combine a broad class of discriminative and generative models, interpo- lating between the two extremes with a multi- conditional likelihood objective. Unlike previ- ous approaches, we couple the two components through shared latent variables, and train using recent advances in variational inference. Instanti- ating our framework with modern deep architec- tures gives rise to deep hybrid models, a highly flexible family that generalizes several existing models and is effective in the semi-supervised setting, where it results in improvements over the state of the art on the SVHN dataset. %Z Reissued by PMLR on 04 October 2026.
APA
Kuleshov, V. & Ermon, S.. (2017). Deep Hybrid Models: Bridging Discriminative and Generative Approaches. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:271-280 Available from https://proceedings.mlr.press/r15/kuleshov17a.html. Reissued by PMLR on 04 October 2026.

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