Vincent Fortuin
Vincent Fortuin
PhD student, ETH Zürich
Verifierad e-postadress på - Startsida
TitelCiteras avÅr
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
V Fortuin, M Hüser, F Locatello, H Strathmann, G Rätsch
International Conference on Learning Representations, 2018
Deep Mean Functions for Meta-Learning in Gaussian Processes
V Fortuin, G Rätsch
arXiv preprint arXiv:1901.08098, 2019
GP-VAE: Deep Probabilistic Time Series Imputation
V Fortuin, D Baranchuk, G Rätsch, S Mandt
arXiv preprint arXiv:1907.04155, 2019
Scalable Gaussian Processes on Discrete Domains
V Fortuin, G Dresdner, H Strathmann, G Rätsch
arXiv preprint arXiv:1810.10368, 2018
InspireMe: Learning Sequence Models for Stories
V Fortuin, RM Weber, S Schriber, D Wotruba, MH Gross
The Thirtieth AAAI Conference on Innovative Applications of Artificial …, 2018
On the Connection between Neural Processes and Gaussian Processes with Deep Kernels
TGJ Rudner, V Fortuin, YW Teh, Y Gal
Bayesian Deep Learning workshop at NeurIPS 2018, 2018
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
bioRxiv, 356477, 2018
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
Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps
L Manduchi, M Hüser, G Rätsch, V Fortuin
arXiv preprint arXiv:1910.01590, 2019
Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets
A Georgiou, V Fortuin, H Mustafa, G Rätsch
arXiv preprint arXiv:1909.13146, 2019
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis
M Rosnati, V Fortuin
arXiv preprint arXiv:1909.12637, 2019
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Artiklar 1–11