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…

Preconditioned ICA for Real Data

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

IVA: Independent Vector Analysis implementation

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

Heart Rate Detection using Web Camera

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

Several maximum likelihood ICA algorithms, including Picard

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 (Published in EMNLP 2023)

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)

Independent Vector Analysis (IVA-G and IVA-L-SOS) implemented in Python

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

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

Blind source separation of real signals

Independent component analysis for dimensionality reduction of hyperspectral images

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