About how to use on seq2seq works about torchcde HOT 5 OPEN

Seq2seq is possible, e.g. just use the final value of a CDE as the initial condition of an ODE. You can return multiple values from cdeint by passing a vector of integration times.

from torchcde.

Hi @patrick-kidger ,
First of all, thank you so much for sharing your work with us.
I have a doubt regarding the CDE and ODE blocks. Could you please give an example of how exactly the architecture of the CDE and ODE blocks will look like in the Seq2Seq regression task if possible?
Thank you very much!

from torchcde.

Hi @patrick-kidger , First of all, thank you so much for sharing your work with us. I have a doubt regarding the CDE and ODE blocks. Could you please give an example of how exactly the architecture of the CDE and ODE blocks will look like in the Seq2Seq regression task if possible? Thank you very much!

@pruthamodak I have created a repo TANODE for studying how to use ODE-based models in timeseries forecasting. It shows ODE-RNN (see paper) models in Recurrent Arch, Seq2Seq Arch and Variational AutoEncoders. CDEs are in TODO list.

You can change Args["arch"] in run_models.py to "Recurrent", "Seq2Seq" or "VAE", and Args["using"] should be "ODE_RNN". Please forgive me for not writing README until I finish my research!

from torchcde.

Hi @patrick-kidger , First of all, thank you so much for sharing your work with us. I have a doubt regarding the CDE and ODE blocks. Could you please give an example of how exactly the architecture of the CDE and ODE blocks will look like in the Seq2Seq regression task if possible? Thank you very much!

@pruthamodak I have created a repo TANODE for studying how to use ODE-based models in timeseries forecasting. It shows ODE-RNN (see paper) models in Recurrent Arch, Seq2Seq Arch and Variational AutoEncoders. CDEs are in TODO list.

You can change Args["arch"] in run_models.py to "Recurrent", "Seq2Seq" or "VAE", and Args["using"] should be "ODE_RNN". Please forgive me for not writing README until I finish my research!

Hello! The repo TANODE is not found in the github.

from torchcde.

Hi @patrick-kidger , First of all, thank you so much for sharing your work with us. I have a doubt regarding the CDE and ODE blocks. Could you please give an example of how exactly the architecture of the CDE and ODE blocks will look like in the Seq2Seq regression task if possible? Thank you very much!

@pruthamodak I have created a repo TANODE for studying how to use ODE-based models in timeseries forecasting. It shows ODE-RNN (see paper) models in Recurrent Arch, Seq2Seq Arch and Variational AutoEncoders. CDEs are in TODO list.
You can change Args["arch"] in run_models.py to "Recurrent", "Seq2Seq" or "VAE", and Args["using"] should be "ODE_RNN". Please forgive me for not writing README until I finish my research!

Hello! The repo TANODE is not found in the github.

Sorry for changing this repo private (our research team is testing some unstable features)

Here is a public branch: Forecasting with Neural ODEs. I add a README file.

from torchcde.