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Modeling Complex Disease Trajectories using Deep Generative Models with
  Semi-Supervised Latent Processes

Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes

14 November 2023
Cécile Trottet
Manuel Schürch
Ahmed Allam
Imon Barua
L. Petelytska
Oliver Distler
A. Hoffmann-Vold
Michael Krauthammer
Eustar collaborators
ArXivPDFHTML

Papers citing "Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes"

3 / 3 papers shown
Title
SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent
  Factor Swapping
SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping
Jiageng Zhu
Hanchen Xie
Wael AbdAlmageed
SSL
CoGe
DRL
18
4
0
21 Sep 2022
Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use
  Case
Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case
Clément Chadebec
Louis J. Vincent
S. Allassonnière
DRL
27
27
0
16 Jun 2022
Weakly-Supervised Disentanglement Without Compromises
Weakly-Supervised Disentanglement Without Compromises
Francesco Locatello
Ben Poole
Gunnar Rätsch
Bernhard Schölkopf
Olivier Bachem
Michael Tschannen
CoGe
OOD
DRL
171
311
0
07 Feb 2020
1