1 citations · 1 across the 9 of their papers we have counts for
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Geometric Moment Alignment for Domain Adaptation via Siegel Embeddings
Shayan Gharib, Marcelo Hartmann, Arto Klami
We address the problem of distribution shift in unsupervised domain adaptation with a moment-matching approach. Existing methods typically align low-order statistical moments of th…
Simplex-to-Euclidean Bijections for Categorical Flow Matching
Bernardo Williams, Victor M. Yeom-Song, Marcelo Hartmann +1
We propose a method for learning and sampling from probability distributions supported on the simplex. Our approach maps the open simplex to Euclidean space via smooth bijections,…
Learning Geometry and Topology via Multi-Chart Flows
Hanlin Yu, Søren Hauberg, Marcelo Hartmann +2
Real world data often lie on low-dimensional Riemannian manifolds embedded in high-dimensional spaces. This motivates learning degenerate normalizing flows that map between the amb…