Пожалуйста, войдите в систему, чтобы принять участие в дискуссии (добавить собственные рецензию, или комментарий)
Цитировать эту публикацию
%0 Conference Paper
%1 ramchandran2021longitudinal
%A Ramchandran, Siddharth
%A Tikhonov, Gleb
%A Kujanpää, Kalle
%A Koskinen, Miika
%A Lähdesmäki, Harri
%B International Conference on Artificial Intelligence and Statistics
%D 2021
%K imputation gain missing data generative model net nn deep learning autoencoder variational longitudinal temporal timeseries time series citedby:scholar:count:6 citedby:scholar:timestamp:2022-5-13
%P 3898--3906
%T Longitudinal variational autoencoder
@inproceedings{ramchandran2021longitudinal,
added-at = {2022-05-14T01:13:25.000+0200},
author = {Ramchandran, Siddharth and Tikhonov, Gleb and Kujanp{\"a}{\"a}, Kalle and Koskinen, Miika and L{\"a}hdesm{\"a}ki, Harri},
biburl = {https://www.bibsonomy.org/bibtex/2bdfff5b60d95dd0c8a68a2529c359861/becker},
booktitle = {International Conference on Artificial Intelligence and Statistics},
interhash = {4d5bee04f3420bfb179a20096bd515ad},
intrahash = {bdfff5b60d95dd0c8a68a2529c359861},
keywords = {imputation gain missing data generative model net nn deep learning autoencoder variational longitudinal temporal timeseries time series citedby:scholar:count:6 citedby:scholar:timestamp:2022-5-13},
organization = {PMLR},
pages = {3898--3906},
timestamp = {2022-05-14T01:13:25.000+0200},
title = {Longitudinal variational autoencoder},
year = 2021
}