Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions
arXiv:2610.31328v1 Announce Type: new Abstract: Deep learning (DL) methods have substantially improved predictive performance across a wide range of downstream prediction tasks, enabling new discoveries in multiple domains. However, principled DL methods for non-Euclidean random objects remain rela…
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- 2026-10-23 04:00 · arXiv stat.ML
Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions