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AbstractPhil
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https://civitai.com/user/AbstractPhila

AI & ML interests

datasets, research papers, experimentation, vision, classification, text encoders, tokenization, llms, diffusion, distillation, and more.

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posted an update about 18 hours ago
My recent study in a nutshell shows a few important elements and everything else is technical. * There are most definitely invariant architectural geometric states that persist and can be taught. * They are not coincidental and the process works effectively on multiple data types and processes, not just noise. Noise is just fast to test with. * Systems like SVD, Eigh, Conv, and the like - HELP align those systems for larger structures to produce amplified stability, but are not required for smaller structures, and the tests show even attention gets in the way at the smallest. * Batched arrays, stacks, queues, and so on - all improve performance depending on the task. * An SVAE battery is resolution agnostic, meaning with simple processing and logic you can scan space and record meshes fairly optimally to record large amounts of inference data. * Batteries when trained on one specific task often can be directly used for other tasks once a codebook is fitted with the necessary data. Meaning a battery trained on gaussian noise can be fed imagenet snippets and downstream the MSE rates from the 64 battery array can be consumed for statistics aggregation to a fair degree of accuracy without actually training the array on images themselves. * The battery codebook is a pointwise rigid map within the battery and can be used for pairwise learning when using the H2, H2a, and H2b batteries. So this is, the evolved state of the geometric vocabulary in some ways, and a completely new and unexpected systemic development in others. They stack, you can reuse them, so small you can swap them at runtime with no time loss, they align rapidly, and downstream tasks can consume their information. There are many untested avenues that I need to make a full writeup for because quite frankly it's messy currently and Claude is only making it more messy instead of cleaner.
upvoted an article about 19 hours ago
The Polygonal Omega: Trained Sphere-Solvers Are Projective Codebooks
published an article about 19 hours ago
The Polygonal Omega: Trained Sphere-Solvers Are Projective Codebooks
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