Vincent Fortuin
Vincent Fortuin
Postdoctoral researcher, University of Cambridge
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GP-VAE: Deep Probabilistic Multivariate Time Series Imputation
V Fortuin, D Baranchuk, G Rätsch, S Mandt
AISTATS 2020, 2020
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
V Fortuin, M Hüser, F Locatello, H Strathmann, G Rätsch
ICLR 2019, 2018
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
J Rothfuss, V Fortuin, M Josifoski, A Krause
ICML 2021, 2021
Bayesian Neural Network Priors Revisited
V Fortuin, A Garriga-Alonso, F Wenzel, G Rätsch, RE Turner, ...
ICLR 2022, 2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
A Immer, M Bauer, V Fortuin, G Rätsch, ME Khan
ICML 2021, 2021
Meta-Learning Mean Functions for Gaussian Processes
V Fortuin, H Strathmann, G Rätsch
NeurIPS 2019 workshop on Bayesian Deep Learning, 2019
Conservative Uncertainty Estimation By Fitting Prior Networks
K Ciosek, V Fortuin, R Tomioka, K Hofmann, R Turner
ICLR 2020, 2020
Priors in bayesian deep learning: A review
V Fortuin
International Statistical Review, 2022
Repulsive Deep Ensembles are Bayesian
F D'Angelo, V Fortuin
NeurIPS 2021, 2021
Exact Langevin Dynamics with Stochastic Gradients
A Garriga-Alonso, V Fortuin
AABI 2021, 2021
T-DPSOM-An Interpretable Clustering Method for Unsupervised Learning of Patient Health States
L Manduchi, M Hüser, M Faltys, J Vogt, G Rätsch, V Fortuin
ACM CHIL 2021, 2021
Sparse Gaussian process variational autoencoders
M Ashman, J So, W Tebbutt, V Fortuin, M Pearce, RE Turner
arXiv preprint arXiv:2010.10177, 2020
Sparse Gaussian processes on discrete domains
V Fortuin, G Dresdner, H Strathmann, G Rätsch
IEEE Access 9, 76750-76758, 2021
Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data
A Kopf, V Fortuin, VR Somnath, M Claassen
PLOS Computational Biology, 2019
On Stein Variational Neural Network Ensembles
F D'Angelo, V Fortuin, F Wenzel
ICML 2021 workshop on Uncertainty and Robustness in Deep Learning, 2021
BNNpriors: A library for Bayesian neural network inference with different prior distributions
V Fortuin, A Garriga-Alonso, M van der Wilk, L Aitchison
Software Impacts, 2021
Scalable Gaussian Process Variational Autoencoders
M Jazbec, M Ashman, V Fortuin, M Pearce, S Mandt, G Rätsch
AISTATS 2021, 2021
On the Connection between Neural Processes and Gaussian Processes with Deep Kernels
TGJ Rudner, V Fortuin, YW Teh, Y Gal
NeurIPS 2018 workshop on Bayesian Deep Learning, 2018
Data augmentation in Bayesian neural networks and the cold posterior effect
S Nabarro, S Ganev, A Garriga-Alonso, V Fortuin, M van der Wilk, ...
Uncertainty in Artificial Intelligence, 1434-1444, 2022
Sparse moes meet efficient ensembles
JU Allingham, F Wenzel, ZE Mariet, B Mustafa, J Puigcerver, N Houlsby, ...
arXiv preprint arXiv:2110.03360, 2021
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