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In this paper, we introduce directed networks called `divergence network in order to perform graphical calculation of divergence functions. By using the divergence networks, we can easily understand the geometric meaning of calculation results and grasp relations among divergence functions intuitively.
We provide a unifying view of statistical information measures, multi-way Bayesian hypothesis testing, loss functions for multi-class classification problems, and multi-distribution $f$-divergences, elaborating equivalence results between all of thes
To help understand the underlying mechanisms of neural networks (NNs), several groups have, in recent years, studied the number of linear regions $ell$ of piecewise linear functions generated by deep neural networks (DNN). In particular, they showed
Classical linear metric learning methods have recently been extended along two distinct lines: deep metric learning methods for learning embeddings of the data using neural networks, and Bregman divergence learning approaches for extending learning E
A recently introduced canonical divergence $mathcal{D}$ for a dual structure $(mathrm{g}, abla, abla^*)$ is discussed in connection to other divergence functions. Finally, open problems concerning symmetry properties are outlined.
Graphical models are useful tools for describing structured high-dimensional probability distributions. Development of efficient algorithms for learning graphical models with least amount of data remains an active research topic. Reconstruction of gr