Vincent Fortuin
Vincent Fortuin
PhD student, ETH Zürich
Verified email at inf.ethz.ch - Homepage
Title
Cited by
Cited by
Year
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
V Fortuin, M Hüser, F Locatello, H Strathmann, G Rätsch
ICLR 2019, 2018
582018
GP-VAE: Deep Probabilistic Multivariate Time Series Imputation
V Fortuin, D Baranchuk, G Rätsch, S Mandt
AISTATS 2020, 2020
48*2020
Meta-Learning Mean Functions for Gaussian Processes
V Fortuin, H Strathmann, G Rätsch
NeurIPS 2019 workshop on Bayesian Deep Learning, 2019
14*2019
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
J Rothfuss, V Fortuin, A Krause
ICML 2020 workshop on Lifelong Learning, 2020
82020
Scalable Gaussian Processes on Discrete Domains
V Fortuin, G Dresdner, H Strathmann, G Rätsch
NeurIPS 2018 workshop on Bayesian Nonparametrics, 2018
72018
Conservative Uncertainty Estimation By Fitting Prior Networks
K Ciosek, V Fortuin, R Tomioka, K Hofmann, R Turner
ICLR 2020, 2020
52020
DPSOM: Deep probabilistic clustering with self-organizing maps
L Manduchi, M Hüser, J Vogt, G Rätsch, V Fortuin
NeurIPS 2019 workshop on Machine Learning for Health, 2019
52019
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
52018
InspireMe: Learning Sequence Models for Stories
V Fortuin, RM Weber, S Schriber, D Wotruba, MH Gross
AAAI 2018, 2018
52018
Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations
A Kopf, V Fortuin, VR Somnath, M Claassen
arXiv preprint arXiv:1910.07763, 2019
42019
Supervised learning on synthetic data for reverse engineering gene regulatory networks from experimental time-series
S Ganscha, V Fortuin, M Horn, E Arvaniti, M Claassen
NeurIPS 2019 workshop on Machine Learning in Computational Biology, 2019
32019
Bayesian Neural Network Priors Revisited
V Fortuin, A Garriga-Alonso, F Wenzel, G Rätsch, RE Turner, ...
AABI 2021, 2021
22021
Scalable Gaussian Process Variational Autoencoders
M Jazbec, M Ashman, V Fortuin, M Pearce, S Mandt, G Rätsch
AISTATS 2021, 2021
22021
Sparse Gaussian Process Variational Autoencoders
M Ashman, J So, W Tebbutt, V Fortuin, M Pearce, RE Turner
arXiv preprint arXiv:2010.10177, 2020
22020
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis
M Rosnati, V Fortuin
arXiv preprint arXiv:1909.12637, 2019
22019
Exact Langevin Dynamics with Stochastic Gradients
A Garriga-Alonso, V Fortuin
AABI 2021, 2021
12021
Factorized Gaussian Process Variational Autoencoders
M Jazbec, M Pearce, V Fortuin
AABI 2021, 2021
12021
META : Memory-efficient taxonomic classification and abundance estimation for metagenomics with deep learning
A Georgiou, V Fortuin, H Mustafa, G Rätsch
Machine Learning for Computational Biology 2019, 2019
1*2019
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
A Immer, M Bauer, V Fortuin, G Rätsch, ME Khan
arXiv preprint arXiv:2104.04975, 2021
2021
On Disentanglement in Gaussian Process Variational Autoencoders
S Bing, V Fortuin, G Rätsch
arXiv preprint arXiv:2102.05507, 2021
2021
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