timsainb / ParametricUMAP_paper
Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).
☆150Updated 4 years ago
Alternatives and similar repositories for ParametricUMAP_paper:
Users that are interested in ParametricUMAP_paper are comparing it to the libraries listed below
- Uniform Manifold Approximation with Two-phase Optimization (IEEE VIS 2022 short)☆107Updated 4 months ago
- General-purpose dimensionality reduction and manifold learning tool based on Variational Autoencoder, implemented in TensorFlow.☆158Updated 3 months ago
- ☆33Updated 2 years ago
- Self-Supervised Noise Embeddings (Self-SNE)☆158Updated 2 weeks ago
- Explaining dimensionality results using SHAP values☆54Updated 3 months ago
- Hyperbolic PCA via Horospherical Projections☆70Updated last year
- Multislice PHATE for tensor embeddings☆59Updated 4 years ago
- Contrastive neighbor embeddings☆54Updated last month
- Deep Graph Mapper: Seeing Graphs through the Neural Lens☆58Updated last year
- A Python package for intrinsic dimension estimation☆85Updated 2 months ago
- Vectorizers for a range of different data types☆101Updated 2 months ago
- The need to understand cell developmental processes has spawned a plethora of computational methods for discovering hierarchies from scRN…☆148Updated 3 years ago
- ☆63Updated 5 years ago
- Repo for open sourcing the NAMs.☆25Updated 4 years ago
- Hierarchical Uniform Manifold Approximation and Projection☆233Updated last month
- Contrastive PCA☆210Updated 5 months ago
- Loss Landscapes of Regularized Linear Autoencoders☆144Updated 2 years ago
- Mapper implementation (Topological Data Analysis) in Python☆65Updated 6 years ago
- A repository for explaining feature attributions and feature interactions in deep neural networks.☆187Updated 3 years ago
- Data Augmentation with Variational Autoencoders (TPAMI)☆140Updated 2 years ago
- Training and evaluating NBM and SPAM for interpretable machine learning.☆77Updated 2 years ago
- A practical Active Learning python package with a strong focus on experiments.☆51Updated 2 years ago
- Tools for training explainable models using attribution priors.☆123Updated 4 years ago
- Dimensionality reduction in very large datasets using Siamese Networks☆332Updated 6 months ago
- Codebase for Learning Invariances in Neural Networks☆94Updated 2 years ago
- Unsupervised visualization of image datasets using contrastive learning☆116Updated 3 weeks ago
- A Python package for hubness analysis and high-dimensional data mining☆44Updated 10 months ago
- Density-preserving data visualization tools den-SNE and densMAP☆110Updated 3 years ago
- Code for the paper "Topological Autoencoders" by Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt.☆145Updated 3 years ago
- ☆30Updated 2 years ago