4
votes
Modeling uncertainty from known physics
Yes. I was searching for the same thing a while back and I came across the concept of PINNs.
Physics-informed neural networks (PINNs) are neural networks that encode model equations, such as partial ...
- 1,566
2
votes
Accepted
How to predict what someone will order?
I think this is a problem that may be solved using distribution functions.
I. The mathematical model for Y is a Bernoulli distribution. The Bernoulli distribution ...
- 2,644
2
votes
Modeling uncertainty from known physics
Modeling the non-linear term $λ(s)$ in your equation using PyTorch can be approached as a parameter estimation or function approximation problem. Since you know the true solution for $λ(s)$, you can ...
- 3,360
1
vote
Accepted
What process actually takes place during audio feedback suppression machine learning
The processing learned by neural networks is often referred to as a "black box" because we can't fully characterize it to understand it, that is, it's not "interpretable".
This way,...
- 20.5k
1
vote
What is a better way of loading a model in Flask service hosted with IIS
If you load the time every time you have an incoming request, you would be increasing the latency. Usually, you want to minimize request latency, so it would be better to load the model at the ...
- 20.5k
1
vote
How complex can I make a classifier's loss function in Scikit-Learn?
Looking at this GitHub issue I am afraid that's not possible.
In my experience working with the library, I have never worked on a use case that required such modification, I mostly change the values ...
- 371
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