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Probing as Quantifying the Inductive Bias of Pre-trained Representations


Oct 15, 2021
Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell


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Pathologies in priors and inference for Bayesian transformers


Oct 15, 2021
Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin


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Sparse MoEs meet Efficient Ensembles


Oct 07, 2021
James Urquhart Allingham, Florian Wenzel, Zelda E Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton

* 44 pages, 19 figures, 24 tables 

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Deep Classifiers with Label Noise Modeling and Distance Awareness


Oct 06, 2021
Vincent Fortuin, Mark Collier, Florian Wenzel, James Allingham, Jeremiah Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou


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Neural Variational Gradient Descent


Jul 29, 2021
Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann


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A Bayesian Approach to Invariant Deep Neural Networks


Jul 20, 2021
Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin

* 8 pages, 3 figures, To be published in ICML UDL 2021 

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Repulsive Deep Ensembles are Bayesian


Jun 22, 2021
Francesco D'Angelo, Vincent Fortuin


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On Stein Variational Neural Network Ensembles


Jun 22, 2021
Francesco D'Angelo, Vincent Fortuin, Florian Wenzel


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Data augmentation in Bayesian neural networks and the cold posterior effect


Jun 10, 2021
Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison


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Priors in Bayesian Deep Learning: A Review


May 26, 2021
Vincent Fortuin


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BNNpriors: A library for Bayesian neural network inference with different prior distributions


May 14, 2021
Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison

* Accepted for publication at Software Impacts 

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Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning


May 11, 2021
Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan

* Accepted for publication at ICML 2021 

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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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Annealed Stein Variational Gradient Descent


Feb 08, 2021
Francesco D'Angelo, Vincent Fortuin


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Exact Langevin Dynamics with Stochastic Gradients


Feb 02, 2021
Adrià Garriga-Alonso, Vincent Fortuin

* 13 pages, 2 figures. Accepted to the 3rd Symposium on Advances in Approximate Bayesian Inference (AABI 2021) 

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Factorized Gaussian Process Variational Autoencoders


Nov 14, 2020
Metod Jazbec, Michael Pearce, Vincent Fortuin


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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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Sparse Gaussian Process Variational Autoencoders


Oct 23, 2020
Matthew Ashman, Jonathan So, Will Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner

* 19 pages, 6 figures 

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PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees


Feb 13, 2020
Jonas Rothfuss, Vincent Fortuin, Andreas Krause


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Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations


Oct 17, 2019
Andreas Kopf, Vincent Fortuin, Vignesh Ram Somnath, Manfred Claassen

* Submitted as conference paper at Eighth International Conference on Learning Representations (ICLR 2020) 

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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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