aiqbench analysis
This research introduces Hyperspherical Gaussian Alignment (HGA), a method that aligns independently trained neural networks by optimizing geometric fits between latent spaces rather than relying on paired data anchors. By leveraging hyperspherical geodesic matching, the approach enables effective model stitching and multilingual embedding recovery in unsupervised or weakly supervised regimes. This advancement addresses a fundamental challenge in scaling AI systems where direct compatibility between distinct model architectures is required without extensive supervision.
Unsupervised Latent Space Alignment with Hyperspherical Geodesic Matching
arXiv:2608.28840v1 Announce Type: new Abstract: Independently trained neural networks tend to encode the same data with similar latent geometries. These latent geometries are not directly compatible, yet they can be nearly the same up to some class of transformations. While there exists many methods for alignment between different latent spaces, it is typically done using a set of shared sample correspondences, known as anchors. This leaves a fundamental question: are the geometric signatures of different latent spaces representing similar data sufficient to recover an alignment between them? To that end, we introduce HGA (Hyperspherical Gaussian Alignment), a method that directly optimizes a transformation between two latent spaces by maximizing a geometric measure of "fit" between them. Since it is driven by the geometry of the latent spaces rather than paired data, HGA can operate in both an unsupervised and weakly supervised regime. On tasks such as model stitching or multilingual word embedding correspondence recovery, HGA manages to match supervised results with minimal or no supervision.
Abstract:Independently trained neural networks tend to encode the same data with similar latent geometries. These latent geometries are not directly compatible, yet they can be nearly the same up to some class of transformations. While there exists many methods for alignment between different latent spaces, it is typically done using a set of shared sample correspondences, known as anchors. This leaves a fundamental question: are the geometric signatures of different latent spaces representing similar data sufficient to recover an alignment between them? To that end, we introduce HGA (Hyperspherical Gaussian Alignment), a method that directly optimizes a transformation between two latent spaces by maximizing a geometric measure of "fit" between them. Since it is driven by the geometry of the latent spaces rather than paired data, HGA can operate in both an unsupervised and weakly supervised regime. On tasks such as model stitching or multilingual word embedding correspondence recovery, HGA manages to match supervised results with minimal or no supervision.
Machine Learning (cs.LG)
arXiv:2608.28840 [cs.LG]
(or arXiv:2608.28840v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.28840 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 254 words
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