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 fixed latent dimension. We demonstrate Moment Pooling on the collider physics
task of quark/gluon jet classification by extending Energy Flow Networks (EFNs)
to Moment EFNs. We find that Moment EFNs with latent dimensions as small as 1
perform similarly to ordinary EFNs with higher latent dimension. This small
latent dimension allows for the internal representation to be directly
visualized and interpreted, which in turn enables the learned internal jet
representation to be extracted in closed form.

许多机器学习应用涉及到学习数据的潜在表示，本研究提出了 “Moment Pooling” 方法，通过扩展 Deep Sets 网络中的求和操作为任意多元矩操作，显著降低了这些网络的潜在空间维度，同时保持甚至提高了性能，在胶子物理任务中证明了 Moment Pooling 的有效性，并且能够将内部表示直接可视化和解释。