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metric-space-valued regression

topic1 events
papersSEP 10 04:00 UTC

Deep Fréchet Neural Network Framework Proposed for Metric-Space-Valued Regression

Researchers introduce DFNN, a deep neural network framework designed for regression tasks where the response variable lies in a general metric space rather than Euclidean coordinates. The approach targets non-Euclidean outputs such as probability distributions, networks, symmetric positive-definite matrices, and compositional data. It extends Fréchet regression concepts into deep learning architectures for these increasingly common data types.

deep-learning/regression/statistics/metric-spaces