About A collection of AWESOME things about information geometry Topics

Methods for computational information geometry

A Fisher information matrix python package for GW detector networks.

Lecture notes of Professor Stéphane Mallat - Collège de France - Paris

A framework for determining the maximum information gain and optimising experimental design in neutron reflectometry using the Fisher information.

[ICML 2025] Official implementation of "Efficiently Access Diffusion Fisher: Within the Outer Product Span Space".

Active learning for neural networks

Experimental design in neutron reflectometry using the Fisher information.

Minimal effort to get maximal information from your experiments

My PhD thesis: Regression modelling using priors depending on Fisher information covariance kernels (I-priors)

Matlab package for LNA simulations of biological models

A sensitivity toolbox that is tailored to the design process in the presence of uncertainties

An R package for I-prior regression

A hands‑on, first‑principles guide to fitting logistic regression via the Iteratively Reweighted Least Squares (IRLS) algorithm complete with mathematical derivations, R code from scratch, and a real‑world S&P data case study to bring your statistical modeling skills to the next level.

Fisher Flow: A unified information-geometric framework for sequential inference revealing how modern optimizers (Adam, Natural Gradient, K-FAC, EWC) emerge as special cases of Fisher information propagation

An R port of https://github.com/csunlab/fisher-information. For Quinn Asena

Data and code modeling cell signal processing using maximum-likelihood estimators, information theory, and stochastic differential equation simulations for "Physical limits of galvanotaxis"

Official code of the study “LAQFI: Layer-Adaptive Quantization on Diffusion Models using Fisher Information.” Presented as a poster at the 2024 IEIE Symposium (Institute of Electronics and Information Engineers).

Additive interaction modelling using I-priors

Unified theoretical–empirical verification of the Cognitive Uncertainty Principle (CUP). A jump–diffusion model reveals an epistemic phase boundary between a Fisher regime (Δε·ΔDₖₗ ≥ 1.17×10⁻⁴) and a KL regime (Δε·ΔDₖₗ ≈ 1.71×10⁻²). The canonical bound Δβ·KL ≥ 3.94×10⁻⁴ remains robust.