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34 Open 28 Closed
34 Open 28 Closed
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Issues list

how would one support 3 labels: win, loss, censored?
#77 opened Jun 17, 2023 by HananInfiniGrow
preparation data for churn prediction
#76 opened Jan 24, 2022 by maksimusGREEN
1
will it work for multivariate time series prediction both regression and classification
#75 opened Jan 10, 2021 by Sandy4321
Numerical instability parameterization tricks
#73 opened Sep 5, 2020 by kieferk
How to use the model to predict
#70 opened Jan 14, 2020 by wayfarer91
multi variate time series : we have categorical and continues data
#68 opened Dec 5, 2019 by Sandy4321
References of success of the WTTE-RNN structure?
#67 opened Oct 29, 2019 by SoufianeDataFan
Weird Beta outputs
#64 opened Oct 3, 2019 by Darenar
possible memory issue with large data
#63 opened Jul 30, 2019 by baharian
wtte.pipelines.data_pipeline returns wrong seq_ids
#61 opened Jun 9, 2019 by michigann
c-index
#60 opened May 11, 2019 by EdMwa
Log-likelihood for discrete Weibull distribution
#58 opened May 9, 2019 by michael-tsel
3
Loss Function - Not the PCF?
#56 opened Apr 18, 2019 by JonStevensWork
2
Event with duration
#54 opened Feb 14, 2019 by flip111
Bimodal Hazard
#52 opened Nov 4, 2018 by adamhaber-atidot
4
Invalid Loss error when training on the GPU
#51 opened Sep 28, 2018 by dongishan
6
Masking layer doesn't work
#50 opened Sep 10, 2018 by FrancescoFrontino
1
Loss dropping to 1e-6
#49 opened Aug 31, 2018 by taxus13
7
Release wtte 1.1.2
#48 opened Aug 16, 2018 by ragulpr
5 tasks
@ragulpr
Travis CI fail Python 3.6 with "KERAS_BACKEND=theano" on develop bug
#46 opened Aug 15, 2018 by ragulpr
There's a Kaggle calling out for wtte-rnn weibull
#45 opened Aug 14, 2018 by chris-english
3
AttributeError: module 'numpy' has no attribute 'float128'
#44 opened Jul 28, 2018 by taxus13
4
nan loss function when b approaches 0
#39 opened Jan 23, 2018 by gm-spacagna
2
Question about preprocessing functions
#37 opened Dec 28, 2017 by adam-haber
1
Different "knobs" to improve accuracy
#36 opened Dec 13, 2017 by adam-haber
6
ProTip! Follow long discussions with comments:>50.