Comments (3)
Since a time-dependent ODE can be written as a special case of a time-independent ODE (#9), often the difference is minimal given that we're modeling the hidden state which can be set to any dimensionality.
The original paper used an architecture that appended t to the inputs (as in https://github.com/rtqichen/ffjord/blob/master/lib/layers/diffeq_layers/basic.py#L45) but during reproduction I found that ignoring the dependence on t worked just as well for MNIST which isn't surprising since it's a simple dataset. For planar CNFs, a hypernetwork was used.
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Not an author, but the method suggested in the paper for achieving time dependence of weights is using a Hypernetwork.
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Still you took the opportunity to integrate t everywhere ? Is it not minimal afterall ?
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Related Issues (20)
- reuse solver object
- Integrate Forced Andronov-Hopf Bifurcation HOT 3
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