Title: $\textit{All the World's a Sphere}$: Learning Expressive Hierarchical Representations with Isotropic Hyperspherical Embeddings
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: hierarchical representation, hyperspherical embeddings, geometrical optimization

Abstract: Most existing embedding frameworks rely on Euclidean geometry, which, while effective for modeling symmetric similarity, struggle to represent richer relational structures such as asymmetry, hierarchy, and transitivity. Although alternatives like hypercubes and ellipsoids introduce containment-based semantics, they often suffer from axis-aligned rigidity, anisotropic bias, and high parameter overhead. To address these limitations, we propose SpheREx ($\textbf{Sphe}$rical $\textbf{R}$epresentations for Hierarchical $\textbf{Ex}$pressiveness), a geometric embedding framework that utilizes isotropic hyperspheres for hierarchical and asymmetrical relation representation. By representing entities as hyperspheres, SpheREx naturally models containment, intersection, and mutual exclusion while maintaining rotational invariance and closed-form inclusion criteria. We formally characterize the geometric and probabilistic properties of hyperspherical interactions and show that they capture desirable logical structures. To ensure stable optimization and prevent uncontrolled radius growth, we introduce a volume clipping and radius regularization strategy tailored for asymmetric tasks. We conduct extensive evaluations across four diverse real-world benchmarks, spanning both text and vision modalities. SpheREx consistently outperforms twelve competitive baselines, achieving statistically significant improvements across key evaluation measures. Ablations supported by qualitative analysis across benchmarks demonstrate the efficacy of hyperspheres over state-of-the-art geometric baselines.

TL;DR: Hyperspherical embeddings for hierarchical expressiveness