BorgwardtLab / topological-autoencodersLinks
Code for the paper "Topological Autoencoders" by Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt.
β151Updated 3 years ago
Alternatives and similar repositories for topological-autoencoders
Users that are interested in topological-autoencoders are comparing it to the libraries listed below
Sorting:
- The essence of my research, distilled for reusability. Enjoy π₯!β71Updated last year
- Implementation of the PersLay layer for persistence diagramsβ84Updated 2 years ago
- A topological machine learning framework based on PyTorchβ197Updated last month
- Topological Graph Neural Networks (ICLR 2022)β124Updated 3 years ago
- Persistence differentiation with Gudhi and Tensorflowβ19Updated 2 years ago
- Python-based persistent homology algorithmsβ19Updated 2 years ago
- Code for Optimal Transport for structured data with application on graphsβ103Updated 2 years ago
- Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transportβ45Updated 2 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).β35Updated 4 years ago
- Code for JMLR paper ``Learning Representations of Persistence Barcodes``β24Updated 5 years ago
- Ripser++: GPU-accelerated computation of VietorisβRips persistence barcodesβ118Updated 2 years ago
- This code accompanies the paper "Persistence Images: A Stable Vector Representation of Persistent Homology".β44Updated 5 years ago
- GitHub repository for the ICLR Computational Geometry & Topology Challenge 2021β54Updated 3 years ago
- Statistics on the space of asymmetric networks via Gromov-Wasserstein distanceβ15Updated 5 years ago
- Distances and representations of persistence diagramsβ132Updated last week
- β28Updated last year
- This is an official repository for "Learning topology-preserving data representations" presented at ICLR 2023 conference.β35Updated 2 years ago
- Geometry Regularized Autoencoders (GRAE) for large-scale visualization and manifold learningβ22Updated last year
- Implemented Machine Learning Algorithms in Hyperbolic Geometry (MDS, K-Means, Support vector machines, etc.)β141Updated 5 years ago
- Official PyTorch implementation of π MFCVAE π: "Multi-Facet Clustering Variatonal Autoencoders (MFCVAE)" (NeurIPS 2021). A class of varβ¦β40Updated 2 years ago
- Deep learning made topological.β90Updated last year
- Code of our NeurIPS 2020 publication 'Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence'β25Updated 5 years ago
- A Python package for intrinsic dimension estimationβ94Updated 2 months ago
- Synthetic data sets apt for Topological Data Analysisβ36Updated last week
- Vectorization of persistence diagrams and approximate Wasserstein distanceβ28Updated 5 years ago
- SCOTT: Synthesizing Curvature Operations and Topological Toolsβ14Updated last month
- Code corresponding to the paper Diffusion Earth Mover's Distance and Distribution Embeddingsβ38Updated last year
- Simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called siβ¦β81Updated 4 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paperβ37Updated 5 years ago
- Deep Graph Mapper: Seeing Graphs through the Neural Lensβ58Updated 2 years ago