Hugging Face Trending Papers
IsoAct: Geometry-Aware Post-hoc Debiasing via Isometric Actions
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arXiv Machine Learning
Oct 28
IsoAct: Geometry-Aware Post-hoc Debiasing via Isometric Actions
IsoAct is a post‑hoc debiasing framework that respects the geometry of non‑Euclidean representations. It identifies nuisance‑predictive directions via Fisher discriminant analysis in tangent coordinates and corrects them using manifold‑native actions that keep the representation on its manifold. Experiments on hyperspherical, hyperbolic, and SE(3) spaces show that IsoAct reduces nuisance recoverability while preserving utility and manifold validity, notably improving kinase‑group recoverability and structural prediction on the KLIFS dataset.
Hugging Face Trending Papers
Sep 29
Fisher-IRG: Fisher-Induced Local Invariant Representation Geometry across Language and Vision Models
Semantic-preserving transformations can induce substantial motion in learned representations, while small changes may strongly affect model predictions, raising a basic question: what local metric bes...
arXiv Machine Learning
Sep 30
Fisher-IRG: Fisher-Induced Local Invariant Representation Geometry across Language and Vision Models
arXiv:2609.36458v1 Announce Type: new Abstract: Semantic-preserving transformations can induce substantial motion in learned representations, while small changes may strongly affect model predictions...
arXiv Machine Learning
Sep 23
Discovering Data Manifold Geometry through Geometric Properties
arXiv:2602.02611v2 Announce Type: replace Abstract: A prevailing paradigm in modern representation learning is the map-first approach, in which a representation map is learned from reconstruction, em...
arXiv Machine Learning
Sep 7
Nested Inductive Bias Framework for SPD Manifold Learning
The paper introduces a Nested Inductive Bias framework that uses a two‑stage diffeomorphic composition to pull back non‑Euclidean target geometries onto symmetric positive definite (SPD) manifolds. This approach allows the construction of curvature‑aligned Riemannian classifiers that respect both matrix constraints and the intrinsic relational geometry of data. Empirical results on kinematic, signal processing, and synthetic benchmarks show that class separability degrades when metric curvature does not match the data distribution, and the authors also propose the Rational Conformal Metric (RCM) for robust vectorized architectures.
arXiv Computer Vision
Oct 22
How Representational Priors Shape Generalization in World Action Models
arXiv:2610.29168v1 Announce Type: cross Abstract: World action models (WAMs) are shown to substantially diverge under generalization performance based on their generative backbones. We investigate th...