Comments (2)
Neural CDEs are appropriate for tasks like classification and regression.
For generative problems, Neural SDEs are probably a better fit. For example see the paper Neural SDEs as Infinite-Dimensional GANs, and a worked code example in torchsde.
from torchcde.
Thanks for your valubale advise
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Related Issues (20)
- setup.py and torchsde HOT 1
- Breaking install HOT 4
- publish `torchcde` on PyPI HOT 1
- Consider opening a GH discussions HOT 13
- Online prediction tasks needs examples HOT 4
- About how to use on seq2seq works HOT 5
- Sequence outputs from Neural ODE (similar to 'many to many' RNN)? HOT 3
- CDEs with Image Data HOT 1
- Comparison to alternative ODE models HOT 4
- About how to use CDE in Variational Autoencoders? HOT 1
- Integration to pytorch lighting pipeline HOT 1
- piping in & predicting arbitrary streams of values
- Very slow training with market data, normal? HOT 1
- much slower when using torchsde as backend. HOT 2
- Integrate the ODE function of the CDE system to infinity HOT 2
- Please consider create conda distribution (maybe conda-forge?) HOT 1
- Prediction of irregular time series HOT 1
- Masking Coefficients? HOT 2
- Seq2seq forecasting: adding temporal information and extrapolation
- Overfitting
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