Python toolbox for sampling Determinantal Point Processes
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Updated
Aug 14, 2024 - Python
Python toolbox for sampling Determinantal Point Processes
Matlab Notebook for visualizing random matrix theory results and their applications to machine learning
Python for Random Matrix Theory: cleaning schemes for noisy correlation matrices.
A Julia package for numerical computation in quantum information theory
Python library for Random Matrix Theory, cleaning schemes for correlation matrices, and portfolio optimization
A Library for Denoising Single-Cell Data with Random Matrix Theory
Parallel random matrix tools and complexity for deep learning
A Random Matrix Approach to Extreme Learning Machine
A Random Matrix Approach for Random Feature Maps
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)
Website for the ICML 2021 tutorial on Random Matrix Theory and Machine Learning
A Random Matrix Approach for Least Squares SVM Analysis
A clusterability measure for scRNA-seq data.
An R package for simulating random matrices and ensembles as well as computing and analyzing their eigenvalue spectra and dispersions.
Memo's research works.
Coupled-channels calculation for fusion reaction and quasi-elastic scattering with taking into account noncollective excitations.
Based on Allesina et al. (2017) and Bairey et al. (2016)
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.
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