The paper introduces entropy-ordered flows (EOFlows), a normalizing flow framework that adds an orthogonality regularizer on the decoder Jacobian to promote geometric disentanglement. EOFlows produce near‑orthogonal curvilinear coordinates whose features can be ranked by explained manifold entropy, providing a nonlinear analogue to PCA. The method yields an order‑of‑magnitude more stable features than existing approaches, categorizing them into global, local, and generic types, and can be trimmed to form an autoencoder with competitive rate‑distortion performance.
By Daniel Galperin, Ullrich K\"othe
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...
By Junha Chun, Youngjoon Jeong, Taesup Kim
JEPAMatch introduces a new semi‑supervised learning framework that replaces traditional output‑thresholding with explicit geometric shaping of latent representations. By combining the FlexMatch loss with a latent‑space regularization inspired by LeJEPA, the method encourages isotropic Gaussian structure in the embedding space, mitigating class imbalance and noisy pseudo‑labels. Experiments on CIFAR‑100, STL‑10, and Tiny‑ImageNet show consistent performance gains and faster convergence compared to existing FixMatch‑based baselines.
By Ali Aghababaei-Harandi, Aude Sportisse, Massih-Reza Amini
arXiv:2605. 13352v2 Announce Type: replace Abstract: Standard dual-encoder vision-language models that map images and text to deterministic points on a shared unit hypersphere through $\ell_2$ normalization typically expose neither \emph{aleatoric} uncertainty (cross-modal ambiguity) nor \emph{epistemic} uncertainty (lack of training-distribution support).
By Mayank Nautiyal, Li Ju, Andreas Hellander, Ekta Vats, Prashant Singh
arXiv:2606. 17603v1 Announce Type: new Abstract: In Self-Supervised Learning (SSL), preventing representation collapse by explicitly enforcing a uniform distribution on the unit hypersphere has proven to be effective.
By L\'eo Nicollier (CB, ATT), Enric Meinhardt-Llopis (CB), Max Dunitz (ATT), Marc Pic (ATT), Pablo Mus\'e (CB, IFUMI), Gabriele Facciolo (CB)
arXiv:2410. 10137v5 Announce Type: replace Abstract: We develop Riemannian approaches to variational autoencoders (VAEs) for PDE-type ambient data with regularizing geometric latent dynamics, which we refer to as VAE-DLM, or VAEs with dynamical latent manifolds.
By Andrew Gracyk