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Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models.

J. Bayer, M. Soelch, A. Mirchev, B. Kayalibay, and P. van der Smagt. ICLR, OpenReview.net, (2021)

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Justin Bayer

Justin Lang

Justin Winter

Justin Möhring

Justin Vollmann

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Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF.A. Mirchev, B. Kayalibay, P. van der Smagt, and J. Bayer. ICLR, OpenReview.net, (2021)Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data.M. Karl, M. Soelch, J. Bayer, and P. van der Smagt. ICLR (Poster), OpenReview.net, (2017)Fast Approximate Geodesics for Deep Generative Models.N. Chen, F. Ferroni, A. Klushyn, A. Paraschos, J. Bayer, and P. van der Smagt. ICANN (2), volume 11728 of Lecture Notes in Computer Science, page 554-566. Springer, (2019)Unsupervised Real-Time Control Through Variational Empowerment.M. Karl, P. Becker-Ehmck, M. Soelch, D. Benbouzid, P. van der Smagt, and J. Bayer. ISRR, volume 20 of Springer Proceedings in Advanced Robotics, page 158-173. Springer, (2019)Learning Flat Latent Manifolds with VAEs.N. Chen, A. Klushyn, F. Ferroni, J. Bayer, and P. van der Smagt. ICML, volume 119 of Proceedings of Machine Learning Research, page 1587-1596. PMLR, (2020)Tracking and Planning with Spatial World Models.B. Kayalibay, A. Mirchev, P. van der Smagt, and J. Bayer. L4DC, volume 168 of Proceedings of Machine Learning Research, page 124-137. PMLR, (2022)Filter-Aware Model-Predictive Control.B. Kayalibay, A. Mirchev, A. Agha, P. van der Smagt, and J. Bayer. L4DC, volume 211 of Proceedings of Machine Learning Research, page 1441-1454. PMLR, (2023)Bayesian Learning of Neural Network ArchitecturesG. Dikov, and J. Bayer. Proceedings of Machine Learning Research, volume 89 of Proceedings of Machine Learning Research, page 730--738. PMLR, (16--18 Apr 2019)PRISM: Probabilistic Real-Time Inference in Spatial World Models.A. Mirchev, B. Kayalibay, A. Agha, P. van der Smagt, D. Cremers, and J. Bayer. CoRL, volume 205 of Proceedings of Machine Learning Research, page 161-174. PMLR, (2022)On Fast Dropout and its Applicability to Recurrent Networks.J. Bayer, C. Osendorfer, N. Chen, S. Urban, and P. van der Smagt. ICLR (Poster), (2014)

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Disambiguation of "Bayer, Justin"

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Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models.

Please choose a person to relate this publication to

Justin Bayer

Justin Lang

Justin Winter

Justin Möhring

Justin Vollmann

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Bayer, Justin"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models.

Please choose a person to relate this publication to

Justin Bayer

Justin Lang

Justin Winter

Justin Möhring

Justin Vollmann

Other publications of authors with the same name

Disambiguation

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Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models.