Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…

A UNIFIED SPEECH ENHANCEMENT FRONT-END FOR ONLINE DEREVERBERATION, ACOUSTIC ECHO CANCELLATION, AND SOURCE SEPARATION

Preconditioned ICA for Real Data

IVA: Independent Vector Analysis implementation

This repository contains lecture notes and codes for the course "Computational Methods for Data Science"

A New Perspective of Auxiliary-Function-Based Independent Component Analysis in Acoustic Echo Cancellation

Heart Rate Detection using Web Camera

Several maximum likelihood ICA algorithms, including Picard

This is the code for the paper Embrace the Gap: VAEs perform Independent Mechanism Analysis, showing that optimizing the ELBO is equivalent to optimizing the IMA-regularized log-likelihood under certain assumptions (e.g., small decoder variance).

Create optimized ICA training (OPTICAT) data for EEG data recorded during free viewing (Dimigen, 2020, NeuroImage)

MNE-preprocessing is a python repository to reduce artifacts based on basic and unanimous approaches step by step from electroencephalographic (EEG) raw data.

This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (particularly, with Nonlinear ICA) can be used to extract the causal graph from an underlying structural equation model (SEM).

Discovering Universal Geometry in Embeddings with ICA

Workflow to download, process, and explore microbial RNA-seq data from NCBI SRA

A blind source separation package using non-negative matrix factorization and non-negative ICA

Independent component analysis for dimensionality reduction of hyperspectral images

Blind source separation of real signals

Face Recognition with SVM classifier using PCA, ICA, NMF, LDA reduced face vectors

Code to reproduce the case studies of the 2024 paper "The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology" by Juan L. Gamella, Jonas Peters and Peter Bühlmann.

A Python library for blind source separation.