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Hello, Mr Kidger! This work gives me a lot insight!
I've create a repo Forecast (also mentioned in #39 ). It's not too hard to use CDE in Seq2Seq models, but how to use it in Variational Autoencoders?
Here is a qoutation about modeling uncertainty from your paper:
As presented here, Neural CDEs do not give any measure of uncertainty about their predictions. Such extensions are likely to be possible, given the close links between CDEs and SDEs, and existing work on Neural SDEs.
ODE_RNN arms GRU Cell which can model h_0 and h_0_std. The latter can be viewed as "uncertainty" and is crucial to VAE architecture. Are there related work for how to model uncertainty using CDE?
The text was updated successfully, but these errors were encountered:
Hello, Mr Kidger! This work gives me a lot insight!
I've create a repo Forecast (also mentioned in #39 ). It's not too hard to use CDE in Seq2Seq models, but how to use it in Variational Autoencoders?
Here is a qoutation about modeling uncertainty from your paper:
ODE_RNN arms GRU Cell which can model
h_0
andh_0_std
. The latter can be viewed as "uncertainty" and is crucial to VAE architecture. Are there related work for how to model uncertainty using CDE?The text was updated successfully, but these errors were encountered: