End-to-End Deep Learning for Predicting Metric Space-Valued Outputs 事件

End-to-End Deep Learning for Predicting Metric Space-Valued Outputs arXiv:2509.23544v2 Announce Type: replace-cross Abstract: Many modern applications involve predicting structured, non-Euclidean outputs such as probability distributions, networks, and symmetric positive-definite matrices. These outputs are naturally modeled as elements of general metric spaces, where classical regression techniques that rely on vector space structure no longer apply. We introduce E2M (End-to-End Metric regress