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Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations


Feb 22, 2022
Alexander Immer, Tycho F. A. van der Ouderaa, Vincent Fortuin, Gunnar Rätsch, Mark van der Wilk


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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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