-
The linear optimizer cannot do standardized mean difference, but there is a good approximation it can use, namely:
SMD = (mean(pool) - mean(target))/ (sqrt(var(pool) + var(target))
\approx…
-
**Describe the feature**
Build a quasi-harmonic workflow on top of the EOS Maker.
- [ ] Reuse equation of state maker
- [ ] Adapt the optimizations to constant volume runs with flexible lattice…
JaGeo updated
2 weeks ago
-
This is part of #340.
We have a Laplace approximation, but we only want to use it on a subset of variables (the latent field). We want to use some other inference method on the other variables (hyp…
-
### Describe the bug
I am trying to get a Nystroem approximation of a pre computed kernel but it throws an error if I use n_components anything less than the number of datapoints. Unless my understan…
-
1. Why don't we see an improvement in accuracy when we switch to high order numerical interpolations?
2. Under what conditions can GC tracing get better performance compared to full trajectory tracin…
-
## Description
Provide basic support for an integrated Laplace approximation.
The motivation is to handle hierarchical models with a latent Gaussian model of the form
$\eta \sim p(\eta)$
$\theta…
-
I'm using Deepnest all the time with my waterjet cutter! It's amazingly handy.
That said the approximation of curved surfaces seems very poor and no setting I've found seems to be satisfactory.
…
-
Congratulations on this great piece of work. I have tried to do simple tests like functional approximations and it turns out for a variety of models KANs are poor performing wrt standard MLPs.
It …
-
Hi there! A few months back, I wrote a port of [this gist](https://gist.github.com/MicahElliott/719710) to convert RGB to XTerm256 colours (or their nearest approximation).
**I don't know much abo…
-
What are the quantitative simplifications that capture most of various phenomena/fields? Can things be effectively reduced in order to better understand them? What is the "best" simple understanding o…