Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks
Quick summary
arXiv:2608.12597v1 Announce Type: cross Abstract: Neural networks can often be trained or fine-tuned through random low-dimensional reparameterization, where a small latent vector is mapped into a full parameter update by a frozen random map. This raises a practical question: how large must the latent search space be to reach a low-loss region? We first express the known accessibility transition in an equivalent conic form, centered for compact convex targets at the statistical dimension of the polar cone. Our main theoretical contribution is an orientation-resolved quadratic master formula th
Key takeaways
- arXiv:2608.12597v1 Announce Type: cross Abstract: Neural networks can often be trained or fine-tuned through random low-dimensional reparameterization, where a small latent vector is mapped into a full parameter update by a frozen random map.
- This raises a practical question: how large must the latent search space be to reach a low-loss region?
- We first express the known accessibility transition in an equivalent conic form, centered for compact convex targets at the statistical dimension of the polar cone.
Why it matters
“Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks” is a product decision that may change how people work with AI. Its value depends on task completion, correction effort and data handling—not simply the presence of a new feature.
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