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Bayesian Neural Network Priors Revisited


Feb 12, 2021
Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard Turner, Mark van der Wilk, Laurence Aitchison


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On Disentanglement in Gaussian Process Variational Autoencoders


Feb 10, 2021
Simon Bing, Vincent Fortuin, Gunnar Rätsch


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Scalable Gaussian Process Variational Autoencoders


Nov 12, 2020
Metod Jazbec, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch


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WRSE -- a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU


Nov 02, 2020
Jonathan Heitz, Joanna Ficek, Martin Faltys, Tobias M. Merz, Gunnar Rätsch, Matthias Hüser

* 9 pages, 6 figures 

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A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation


Oct 27, 2020
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

* Journal of Machine Learning Research 2020, Volume 21, Number 209 
* arXiv admin note: substantial text overlap with arXiv:1811.12359 

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A Commentary on the Unsupervised Learning of Disentangled Representations


Jul 28, 2020
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

* The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020 (AAAI-20) 

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Weakly-Supervised Disentanglement Without Compromises


Feb 07, 2020
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen


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Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps


Oct 03, 2019
Laura Manduchi, Matthias Hüser, Gunnar Rätsch, Vincent Fortuin


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Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets


Sep 28, 2019
Andreas Georgiou, Vincent Fortuin, Harun Mustafa, Gunnar Rätsch


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Multivariate Time Series Imputation with Variational Autoencoders


Jul 12, 2019
Vincent Fortuin, Gunnar Rätsch, Stephan Mandt


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Disentangling Factors of Variation Using Few Labels


May 03, 2019
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem


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Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies


May 03, 2019
Stefan G. Stark, Stephanie L. Hyland, Melanie F. Pradier, Kjong Lehmann, Andreas Wicki, Fernando Perez Cruz, Julia E. Vogt, Gunnar Rätsch


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Machine learning for early prediction of circulatory failure in the intensive care unit


Apr 19, 2019
Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean Bodenham, Karsten Borgwardt, Gunnar Rätsch, Tobias M. Merz

* 5 main figures, 1 main table, 13 supplementary figures, 5 supplementary tables; 250ppi images 

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Deep Mean Functions for Meta-Learning in Gaussian Processes


Jan 23, 2019
Vincent Fortuin, Gunnar Rätsch


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Boosting Black Box Variational Inference


Nov 01, 2018
Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch


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Scalable Gaussian Processes on Discrete Domains


Oct 24, 2018
Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch


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Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series


Oct 05, 2018
Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch


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Clustering Meets Implicit Generative Models


Aug 02, 2018
Francesco Locatello, Damien Vincent, Ilya Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf


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On Matching Pursuit and Coordinate Descent


Jul 02, 2018
Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi

* ICML 2018 - Proceedings of the 35th International Conference on Machine Learning 

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Boosting Variational Inference: an Optimization Perspective


Mar 07, 2018
Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch

* AISTATS 2018 

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Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs


Dec 04, 2017
Cristóbal Esteban, Stephanie L. Hyland, Gunnar Rätsch

* 13 pages, 4 figures, 3 tables (update with differential privacy) 

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Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees


Nov 19, 2017
Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi

* NIPS 2017 

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Learning Unitary Operators with Help From u(n)


Jan 10, 2017
Stephanie L. Hyland, Gunnar Rätsch

* 9 pages, 3 figures, 5 figures inc. subfigures, to appear at AAAI-17 

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Bayesian representation learning with oracle constraints


Mar 01, 2016
Theofanis Karaletsos, Serge Belongie, Gunnar Rätsch

* 16 pages, publishes in ICLR 16 

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Knowledge Transfer with Medical Language Embeddings


Feb 10, 2016
Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch

* 6 pages, 2 figures, to appear at SDM-DMMH 2016 

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A Generative Model of Words and Relationships from Multiple Sources


Dec 03, 2015
Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch

* 8 pages, 5 figures; incorporated feedback from reviewers; to appear in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence 2016 

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Automatic Relevance Determination For Deep Generative Models


Aug 26, 2015
Theofanis Karaletsos, Gunnar Rätsch

* equations 8-12 updated 

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