Dynamic attributes / attributes with time stamp? about doppelganger HOT 6 CLOSED

If the 'dynamic attributes' have the same length as features, you can just put them as part of features. Note that 'features' in the code can be multi-dimension and can be mixed with categorical and continuous ones, so it should be flexible enough for you to model those 'dynamic attributes'.

from doppelganger.

Thank you so much for your kind reply! Indeed it should be an option, but in this case, how could I use these dynamic attributes as conditions to guide the conditional generation?

from doppelganger.

The 'dynamic attributes' will then be generated jointly with features; so ideally the correlations between 'dynamic attributes' and features can still be learned. Or do you mean that you want to generate features according to specific given 'dynamic attributes'?

from doppelganger.

Yes, ideally I want to generate features according to specific given 'dynamic attributes', is that possible?

from doppelganger.

I see. It is not possible with this architecture then. The other option is that you pad the 'dynamic attributes' to the same length, and treat that as normal attributes so that you can generate features according to it. (But training might be hard if the total dimension of those 'dynamic attributes' is large.)

from doppelganger.

All right, I think learning a joint distribution could already be very helpful, I will try to modify my problem to see whether it could fit current architecture, thanks a lot!

from doppelganger.