188 citations · 367 across the 31 of their papers we have counts for
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cs.LG2018★ 12 cited
Geodesic Clustering in Deep Generative Models
Tao Yang, Georgios Arvanitidis, Dongmei Fu +2
Deep generative models are tremendously successful in learning low-dimensional latent representations that well-describe the data. These representations, however, tend to much dist…
stat.ML2018★ 15 cited
Only Bayes should learn a manifold (on the estimation of differential geometric structure from data)
Søren Hauberg
We investigate learning of the differential geometric structure of a data manifold embedded in a high-dimensional Euclidean space. We first analyze kernel-based algorithms and show…
stat.ML2018★ 5 cited
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models
Anton Mallasto, Søren Hauberg, Aasa Feragen
Latent variable models (LVMs) learn probabilistic models of data manifolds lying in an \emph{ambient} Euclidean space. In a number of applications, a priori known spatial constrain…