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Hello I was measuring the performance between of torchdiffeq odeint and your symplectic odeint and I get following error in my code segment:
```
class Lambda(nn.Module):
def forward(self,…
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Hi, very interesting work and nice presentation in paper. I'm interested in applying conditional flow matching to the tasks without paired training data. For example, many image restoration tasks do n…
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Hi, I reimplemented the code and found that the adjoint method cost about 5x memory when starting solving adjoint equations. And with direct propagation using dpm solver with about only 10 steps, the …
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It would be great to implement regularization techniques that have been recently been developed for FFJORD. For example,
- [How to Train Your Neural ODE: the World of Jacobian and Kinetic Regulariz…
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i have tried to get it working. But the code of [added train-mlp-to-fit-func-with-n-odes.jl](https://github.com/ettoremessina/differential-equations/commit/8788a1b9eff8595a633d4b19ae2db07d240e47ad) do…
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@ChrisRackauckas have you seen this work by Ricky already?
https://arxiv.org/abs/2009.09457
**"Hey, that's not an ODE": Faster ODE Adjoints with 12 Lines of Code**
Patrick Kidger, Ricky T. Q. C…
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Hi Patrick I do really amazed and appreciate you and your team's work on handling these dynamic systems. As a medical student, what I think might be wrong from a mathematician's perspective so please …
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We are experimenting some models/architectures inspired by the NODE model. Given a point (t,x), the idea is to solve an ODE system whose definition uses a neural network (and also its derivative) and …
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I'm not sure if this is a bug, an error in my code, or by design, but my ODE errors out unless `input_dim == output_dim` at all steps within the `f` function. For example, if I were to apply the layer…