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Vincent Fortuin
Vincent Fortuin
Principal Investigator, Helmholtz AI & TU Munich
Подтвержден адрес электронной почты в домене tum.de - Главная страница
Название
Процитировано
Процитировано
Год
GP-VAE: Deep Probabilistic Multivariate Time Series Imputation
V Fortuin, D Baranchuk, G Rätsch, S Mandt
AISTATS 2020, 2020
359*2020
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
V Fortuin, M Hüser, F Locatello, H Strathmann, G Rätsch
ICLR 2019, 2019
2002019
Bayesian Neural Network Priors Revisited
V Fortuin, A Garriga-Alonso, F Wenzel, G Rätsch, RE Turner, ...
ICLR 2022, 2022
1712022
Priors in bayesian deep learning: A review
V Fortuin
International Statistical Review 90 (3), 563-591, 2022
1562022
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
J Rothfuss, V Fortuin, M Josifoski, A Krause
ICML 2021, 2021
1332021
Repulsive Deep Ensembles are Bayesian
F D'Angelo, V Fortuin
NeurIPS 2021, 2021
1192021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
A Immer, M Bauer, V Fortuin, G Rätsch, ME Khan
ICML 2021, 2021
1172021
Conservative Uncertainty Estimation By Fitting Prior Networks
K Ciosek, V Fortuin, R Tomioka, K Hofmann, R Turner
ICLR 2020, 2020
732020
Meta-Learning Mean Functions for Gaussian Processes
V Fortuin, H Strathmann, G Rätsch
NeurIPS 2019 workshop on Bayesian Deep Learning, 2019
55*2019
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
512019
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
45*2021
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
A Immer, TFA van der Ouderaa, V Fortuin, G Rätsch, M van der Wilk
NeurIPS 2022, 2022
422022
Exact Langevin Dynamics with Stochastic Gradients
A Garriga-Alonso, V Fortuin
AABI 2021, 2021
422021
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
T Papamarkou, M Skoularidou, K Palla, L Aitchison, J Arbel, D Dunson, ...
ICML 2024, 2024
41*2024
MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis
M Rosnati, V Fortuin
PLOS ONE 16 (5), 2021
412021
Data augmentation in Bayesian neural networks and the cold posterior effect
S Nabarro, S Ganev, A Garriga-Alonso, V Fortuin, M van der Wilk, ...
UAI 2022, 2022
352022
On Stein Variational Neural Network Ensembles
F D'Angelo, V Fortuin, F Wenzel
ICML 2021 workshop on Uncertainty and Robustness in Deep Learning, 2021
342021
Annealed Stein Variational Gradient Descent
F D’Angelo, V Fortuin
AABI 2021, 2021
292021
Scalable Gaussian Process Variational Autoencoders
M Jazbec, M Ashman, V Fortuin, M Pearce, S Mandt, G Rätsch
AISTATS 2021, 2021
292021
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
282021
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