mailitics

Tag: spaces

  • On Generation in Metric Spaces

    On Generation in Metric Spaces arXiv:2602.07710v1 Announce Type: new Abstract: We study generation in separable metric instance spaces. We extend the language generation framework from Kleinberg and Mullainathan [2024] beyond countable domains by defining novelty through metric separation and allowing asymmetric novelty parameters for the adversary and the generator. We introduce the $(varepsilon,varepsilon’)$-closure dimension, a…

  • Neural Networks on Symmetric Spaces of Noncompact Type

    Neural Networks on Symmetric Spaces of Noncompact Type arXiv:2601.01097v1 Announce Type: new Abstract: Recent works have demonstrated promising performances of neural networks on hyperbolic spaces and symmetric positive definite (SPD) manifolds. These spaces belong to a family of Riemannian manifolds referred to as symmetric spaces of noncompact type. In this paper, we propose a novel…

  • Siegel Neural Networks

    Siegel Neural Networks arXiv:2511.09577v1 Announce Type: new Abstract: Riemannian symmetric spaces (RSS) such as hyperbolic spaces and symmetric positive definite (SPD) manifolds have become popular spaces for representation learning. In this paper, we propose a novel approach for building discriminative neural networks on Siegel spaces, a family of RSS that is largely unexplored in machine…

  • Functional Adjoint Sampler: Scalable Sampling on Infinite Dimensional Spaces

    Functional Adjoint Sampler: Scalable Sampling on Infinite Dimensional Spaces arXiv:2511.06239v1 Announce Type: new Abstract: Learning-based methods for sampling from the Gibbs distribution in finite-dimensional spaces have progressed quickly, yet theory and algorithmic design for infinite-dimensional function spaces remain limited. This gap persists despite their strong potential for sampling the paths of conditional diffusion processes, enabling…

  • Showcasing Your Work on HuggingFace Spaces

    Showcasing Your Work on HuggingFace Spaces Building an app is exciting – but sharing it is where the real value kicks in. Back when Heroku offered a free tier, deploying demos was effortless. Those days are gone, and finding a simple, free way to showcase machine learning apps has become harder. That’s where Hugging Face…

  • Neural Stochastic Differential Equations on Compact State-Spaces

    Neural Stochastic Differential Equations on Compact State-Spaces arXiv:2508.17090v1 Announce Type: new Abstract: Many modern probabilistic models rely on SDEs, but their adoption is hampered by instability, poor inductive bias outside bounded domains, and reliance on restrictive dynamics or training tricks. While recent work constrains SDEs to compact spaces using reflected dynamics, these approaches lack continuous…

  • Unified Native Spaces in Kernel Methods

    Unified Native Spaces in Kernel Methods arXiv:2501.01825v1 Announce Type: new Abstract: There exists a plethora of parametric models for positive definite kernels, and their use is ubiquitous in disciplines as diverse as statistics, machine learning, numerical analysis, and approximation theory. Usually, the kernel parameters index certain features of an associated process. Amongst those features, smoothness…