manifold-learning

Statistical Machine Intelligence & Learning Engine

🔴 MiniSom is a minimalistic implementation of the Self Organizing Maps

PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding) is a tool for visualizing high dimensional data.

CellRank: dynamics from multi-view single-cell data

Pytorch implementation of Hyperspherical Variational Auto-Encoders

Single cell trajectory detection

Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)

Tensorflow implementation of Hyperspherical Variational Auto-Encoders

Manifold-learning flows (ℳ-flows)

TLDR is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self-supervised learning losses

TorchDR - PyTorch Dimensionality Reduction

Systematically learn and evaluate manifolds from high-dimensional data

A Julia package for manifold learning and nonlinear dimensionality reduction

Tensorflow implementation of adversarial auto-encoder for MNIST

A Framework for Dimensionality Reduction in R

Data Science and Matrix Optimization course

This is the code implementation for the GMML algorithm.

Code for the NeurIPS'19 paper "Guided Similarity Separation for Image Retrieval"

Code and reuslts accompanying the NeurIPS 2022 paper with the title SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG