37
votes
Accepted
What are the practical uses of Neural ODEs?
TL;DR: For time series and density modeling, neural ODEs offer some benefits that we don't know how to get otherwise. For plain supervised learning, there are potential computational benefits, but ...
- 594
5
votes
Accepted
Is my understanding of neural ODE correct?
I think you're close.
The neural net is not the solution to the differential equation $\dot{x} = F(x(t))$, but rather the function governing the dynamics. That is to say, the neural net is $F$, not $...
- 39.6k
2
votes
Accepted
Are Hopfield networks useful?
I think you should look at the Hopfield Networks is All You Need, where they make the case that attention, as used in transformers, works similarly Hopfield networks. Also, they use it in their Modern ...
- 102
1
vote
Neural ODEs gradient calculation for multiple time steps
From the paper:
Most ODE solvers have the option to output the state $\mathbf{z}(t)$ at multiple times. When the loss depends
on these intermediate states, the reverse-mode derivative must be broken ...
- 1,184
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