arXiv Machine Learning
Reparameterization through Coverings and Topological Weight Priors
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Constructing VAE Latent Spaces with Prescribed Topology
arXiv:2606. 07058v1 Announce Type: new Abstract: Variational autoencoders (VAEs) learn low-dimensional latent representations of high-dimensional data.
arXiv Machine Learning
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Variational autoencoders with latent high-dimensional steady geometric flows for dynamics
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.
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Statistically Meaningful Geometry (SMG) Beyond the Euclidean Paradigm, with Application to Generative AI
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arXiv Machine Learning
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Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture - Bridging Predictive and Generative Self-Supervised Learning
arXiv:2603.20111v2 Announce Type: replace Abstract: The Joint-Embedding Predictive Architecture (JEPA) is often seen as a non-generative alternative to likelihood-based self-supervised learning, emph...
arXiv Machine Learning
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Group Invariant Spectral Embedding
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arXiv Machine Learning
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Learning the Geometry of Admissible Hypotheses through Inductive Bias in Training Distributions
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