So this library deliberately mirrors Lux.jl however it is build on the more general idea of learning with parametrized functions not relations
| Lux.jl | Lenticulum.jl | |
|---|---|---|
| architecture | explicit | implicit |
| Learner | f_\theta(x): X\rightarrow Y | R_\theta\subset Z |
| Directionality | Directed: inputs → outputs | must choose directions |
| Wiring | directed acylic graph | arbitrary graphs |
| inference | function evaluation | root finding |
| backpropagation | autodiff | implicit function theorem |
The main workhorse of this library are diffusion models:
You can see some of the results in the tutorials already:
https://mathstruct.github.io/Lenticulum.jl/dev/
or you can even look into the corresponding theory wiki, which is an obsidian vault turned into a website:
It is of course still mostly AI generated, however the core idea is more than validated.
I will not register it anytime soon but feel free to try it out.
Feedback appreciated!