Python toolbox for sampling Determinantal Point Processes

Matlab Notebook for visualizing random matrix theory results and their applications to machine learning

No description provided.

A Julia package for numerical computation in quantum information theory

Python for Random Matrix Theory: cleaning schemes for noisy correlation matrices.

Python library for Random Matrix Theory, cleaning schemes for correlation matrices, and portfolio optimization

Open source code for ICML 2025 Paper: Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias

A Library for Denoising Single-Cell Data with Random Matrix Theory

Parallel random matrix tools and complexity for deep learning

A clusterability measure for scRNA-seq data.

A Random Matrix Approach to Extreme Learning Machine

Generate the Tracy-Widom distribution functions for beta = 1, 2, or 4 in Python

A Random Matrix Approach for Least Squares SVM Analysis

A Random Matrix Approach for Random Feature Maps

A package for Random Matrix Theory

High-Entropy Randomness Research Toolkit. High-Entropy Random Number Generation (HE-RNG).

Minimal code and results from the paper 'Unveiling Order from Chaos by approximate 2-localization of random matrices'

Random matrix theory of polarized light scattering in disordered media

Coupled-channels calculation for fusion reaction and quasi-elastic scattering with taking into account noncollective excitations.

A package for generating and analyzing Aztec diamonds

The random matrix gallery is a curated display of classes of random matrices where the eigenvalue spectrum is known. Each image in the gallery below links to a dedicated python notebook where you can vary the parameters and explore the effect on the resulting spectrum.

Memo's research works.

Monster Group–Riemann Zeta spectral connection: 28,160 testable Lehmer pair predictions. CC-BY-4.0 data, MIT scripts.

Python scripts from paper Optimal cleaning for singular values of cross-covariance matrices, by Florent Benaych-Georges, Jean-Philippe Bouchaud, Marc Potters (see https://arxiv.org/abs/1901.05543)

Random Graphs, Random Matrices, FK Dependent Categorical, Galton-Watson

Wishart,GOE and GUE

Sovereign Proof Engine for Molecular Binding -- spectral geometry verifies every prediction

Independent research study at University of California, San Diego in Random Matrix Theory applied in Machine Learning

Code for my bachelor thesis Quantum chaos on Graphs. The main part of this repository is its root-finding solution. Then, the Nearest Neighbor Distribution is performed for the quantum spectra and also for the spectra of some random matrices.

Random Matrix - Theory and Applications