Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling

arXiv:2403.08854v1 Announce Type: cross
Abstract: Many machine learning applications involve learning a latent representation of data, which is often high-dimensional and difficult to directly interpret. In this work, we propose "Moment Pooling", a natural extension of Deep Sets networks which drastically decrease latent space dimensionality of these networks while maintaining or even improving performance. Moment Pooling generalizes the summation in Deep Sets to arbitrary multivariate moments, which enables the model to achieve a much higher effective latent dimensionality for a …

abstract applications arxiv cs.lg data dimensionality extension hep-ph machine machine learning machine learning applications moments natural networks pooling representation space spaces stat.ml type work