Post
1150
GLIP - Geometric Linear Interpolative Patchwork aka geolip.
https://github.com/AbstractEyes/glip-autoencoder
This is the repo that will contain the next experimental stage, which is based entirely on the research and structural boundaries applied by said research. It'll be a little rigid while I get Claude set up.
In order to directly train these layered topological response patchworks you must install and use the geovocab2, geofractal, and wide_compiler repos.
This is due to the wide_compiler's wide_linear high-speed efficiency for ensemble processing, the geovocab2 factory structure with multiple formulas including highly efficient designs meant for kernel compilation, and a series of reusable utilities in geofractal including some of the more complex losses and difficult to optimally tune gate structures surrounding them.
Many of the underlying formulas are outlined here;
AbstractPhil/geometric-experiment-history
Utilization and training USING the pretrained or untrained geolip patchwork will be as simple as loading the model in pytorch and will not require external dependencies of the geolip package, numpy, or pytorch depending on the task. It will come packaged with recommended losses but I encourage experimentation because I simply cannot cover all spectrums.
Experiments show you can train the patchwork directly with task losses and it retains some useful cohesion, but it will lose all identity without the correct losses making it difficult to task-orient the geometric behavior down the chain.
More details to come as development progresses. The system is coming together and the state of the utilizable autoencoder will be ready within a couple weeks. The entire system is built for convenience and reusability, so the structure will be built similarly to autoencoder systems that currently exist, with a few tweaks here and there for important elements - so the interface will be familiar to those who use it.
https://github.com/AbstractEyes/glip-autoencoder
This is the repo that will contain the next experimental stage, which is based entirely on the research and structural boundaries applied by said research. It'll be a little rigid while I get Claude set up.
In order to directly train these layered topological response patchworks you must install and use the geovocab2, geofractal, and wide_compiler repos.
This is due to the wide_compiler's wide_linear high-speed efficiency for ensemble processing, the geovocab2 factory structure with multiple formulas including highly efficient designs meant for kernel compilation, and a series of reusable utilities in geofractal including some of the more complex losses and difficult to optimally tune gate structures surrounding them.
Many of the underlying formulas are outlined here;
AbstractPhil/geometric-experiment-history
Utilization and training USING the pretrained or untrained geolip patchwork will be as simple as loading the model in pytorch and will not require external dependencies of the geolip package, numpy, or pytorch depending on the task. It will come packaged with recommended losses but I encourage experimentation because I simply cannot cover all spectrums.
Experiments show you can train the patchwork directly with task losses and it retains some useful cohesion, but it will lose all identity without the correct losses making it difficult to task-orient the geometric behavior down the chain.
More details to come as development progresses. The system is coming together and the state of the utilizable autoencoder will be ready within a couple weeks. The entire system is built for convenience and reusability, so the structure will be built similarly to autoencoder systems that currently exist, with a few tweaks here and there for important elements - so the interface will be familiar to those who use it.