%0 Conference Paper %1 pmlr-v89-grover19a %A Grover, Aditya %A Ermon, Stefano %B Proceedings of Machine Learning Research %D 2019 %E Chaudhuri, Kamalika %E Sugiyama, Masashi %I PMLR %K uncertainty variational %P 2514--2524 %T Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization %U http://proceedings.mlr.press/v89/grover19a.html %V 89 %X Compressed sensing techniques enable efficient acquisition and recovery of sparse, highdimensional data signals via low-dimensional projections. In this work, we propose Uncertainty Autoencoders, a learning framework for unsupervised representation learning inspired by compressed sensing. We treat the low-dimensional projections as noisy latent representations of an autoencoder and directly learn both the acquisition (i.e., encoding) and amortized recovery (i.e., decoding) procedures. Our learning objective optimizes for a tractable variational lower bound to the mutual information between the datapoints and the latent representations. We show how our framework provides a unified treatment to several lines of research in dimensionality reduction, compressed sensing, and generative modeling. Empirically, we demonstrate a 32% improvement on average over competing approaches for the task of statistical compressed sensing of high-dimensional datasets.

@inproceedings{pmlr-v89-grover19a, abstract = {Compressed sensing techniques enable efficient acquisition and recovery of sparse, highdimensional data signals via low-dimensional projections. In this work, we propose Uncertainty Autoencoders, a learning framework for unsupervised representation learning inspired by compressed sensing. We treat the low-dimensional projections as noisy latent representations of an autoencoder and directly learn both the acquisition (i.e., encoding) and amortized recovery (i.e., decoding) procedures. Our learning objective optimizes for a tractable variational lower bound to the mutual information between the datapoints and the latent representations. We show how our framework provides a unified treatment to several lines of research in dimensionality reduction, compressed sensing, and generative modeling. Empirically, we demonstrate a 32% improvement on average over competing approaches for the task of statistical compressed sensing of high-dimensional datasets.}, added-at = {2019-12-11T13:56:49.000+0100}, author = {Grover, Aditya and Ermon, Stefano}, biburl = {https://www.bibsonomy.org/bibtex/2b56be6ddbfd1852b2b5e30af855cd5d1/kirk86}, booktitle = {Proceedings of Machine Learning Research}, description = {Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, interhash = {eec2bf81730946b1e5eb8f148672b2d0}, intrahash = {b56be6ddbfd1852b2b5e30af855cd5d1}, keywords = {uncertainty variational}, month = {16--18 Apr}, pages = {2514--2524}, pdf = {http://proceedings.mlr.press/v89/grover19a/grover19a.pdf}, publisher = {PMLR}, series = {Proceedings of Machine Learning Research}, timestamp = {2019-12-11T13:56:49.000+0100}, title = {Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization}, url = {http://proceedings.mlr.press/v89/grover19a.html}, volume = 89, year = 2019 }