Information Geometry and Its Applications

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Universal statistics of Fisher information in deep neural networks: mean field approach (2018, background): “It means that the parameter space of a statistical model forms a Riemannian manifold and the FIM works as its Riemannian metric, as is known in information geometry [33].”
Wasserstein Riemannian geometry of Gaussian densities (2018, background): “[1] and [5], are source of promising researches.”
Aitchison’s Compositional Data Analysis 40 Years on: A Reappraisal (2022, background): “Although this notion of monotonicity has also not received much attention in CoDA, the mathematical properties of dissimilarities fulfilling it are well-studied in information geometry [8].”
On the difference between entropic cost and the optimal transport cost (2019, background): “For the quadratic cost, this notion of divergence coincides with the well-known Bregman divergence and is a fundamental quantity in Information Geometry [2].”