drewwilimitis / Manifold-Learning
Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)
☆232Updated 5 years ago
Alternatives and similar repositories for Manifold-Learning:
Users that are interested in Manifold-Learning are comparing it to the libraries listed below
- Implemented Machine Learning Algorithms in Hyperbolic Geometry (MDS, K-Means, Support vector machines, etc.)☆131Updated 4 years ago
- A Python package for intrinsic dimension estimation☆85Updated last month
- Implementation of the PersLay layer for persistence diagrams☆80Updated last year
- PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks☆437Updated 7 months ago
- Hyperbolic Hierarchical Clustering.☆199Updated last year
- Code for the paper "Topological Autoencoders" by Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt.☆144Updated 3 years ago
- GitHub repository for the ICLR Computational Geometry & Topology Challenge 2021☆52Updated 2 years ago
- A library for optimization on Riemannian manifolds☆109Updated last year
- [NeurIPS 2020] Neural Manifold Ordinary Differential Equations (https://arxiv.org/abs/2006.10254)☆117Updated last year
- Manifold-learning flows (ℳ-flows)☆229Updated 4 years ago
- The collection of papers about combining deep learning and Bayesian nonparametrics☆120Updated 5 years ago
- Distances and representations of persistence diagrams☆127Updated 3 weeks ago
- A set of jupyter notebooks for the practice of TDA with the python Gudhi library together with popular machine learning and data sciences…☆399Updated 2 weeks ago
- Open source library for combining TDA and Machine Learning☆87Updated last year
- ODE2VAE: Deep generative second order ODEs with Bayesian neural networks☆127Updated 7 months ago
- Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.☆434Updated last year
- This repository contains experiments with Neural Ordinary Differential Equations with simulated and real empirical data☆198Updated 5 years ago
- Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data☆208Updated 3 years ago
- Simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called si…☆78Updated 3 years ago
- Topological Data Analysis Routines☆146Updated this week
- Materials of the Nordic Probabilistic AI School 2023.☆89Updated last year
- Ripser++: GPU-accelerated computation of Vietoris–Rips persistence barcodes☆106Updated 2 years ago
- Neural Graph Differential Equations (Neural GDEs)☆200Updated 3 years ago
- Code for the paper "Learning Differential Equations that are Easy to Solve"☆278Updated 3 years ago
- Materials of the Nordic Probabilistic AI School 2021.☆93Updated 3 years ago
- Riemannian Adaptive Optimization Methods with pytorch optim☆909Updated 11 months ago
- Experiments for Neural Flows paper☆94Updated 3 years ago
- A didactic Python library with well-commented and annotated implementations of machine learning algorithms.☆52Updated 4 years ago
- ☆151Updated 2 years ago
- Python package for graph-based clustering and semi-supervised learning☆90Updated 2 weeks ago