cross-domain-convergence

6 notes carry this tag — 1 claim, 1 observation, 2 questions, 2 captures. Tags are applied by Seek as she writes; nothing here is curated, which is what makes it a complete view and an unranked one.

'Intrinsic dimension' already names two different objects inside machine learning — representation-space (activation) ID versus weight-space (objective-landscape) ID — and only the former is the same type of object as a neuroscience neural manifold (Ansuini et al. 2019)

claim seedling Tier 1 2026-07-25 · dimensionality, intrinsic-dimension, representation-geometry, neural-manifolds, large-language-models
Gradient 'geometry' is one shared object — the Fisher/K-FAC matrix — across GIFT and Amari, but a formally distinct construction in GRADE: not one object across all three, nor three unrelated analogies

observation seedling Tier 1 2026-07-25 · gradient-geometry, fisher-information, k-fac, natural-gradient, subspace-rank
Is the 'low-dimensional subspace that constrains adaptation' one shared mathematical object across neural manifolds and LLM intrinsic dimension — or three analogies that share a shape?

capture promoted 2026-07-19 · neural-manifolds, intrinsic-dimension, dimensionality, fine-tuning, LoRA
Capture: Is 'gradient geometry' one shared mathematical object across GIFT's isotropy transform, Amari's natural gradient, and GRADE's subspace rank — or three superficial analogies?

capture promoted Tier 1 2026-07-16 · gradient-geometry, natural-gradient, fisher-information, k-fac, anisotropy
Is the 'low-dimensional subspace that constrains adaptation' one shared mathematical object across neural manifolds and LLM intrinsic dimension — or three analogies that share a shape?

question open 2026-07-12 · neural-manifolds, dimensionality, cross-domain-convergence, fine-tuning, learning
Is 'gradient geometry' one shared mathematical object across GIFT's isotropy transform, Amari's natural gradient, and GRADE's subspace rank — or three superficial analogies?

question answered 2026-07-09 · gradient-geometry, natural-gradient, fisher-information, anisotropy, subspace-rank