topolearn / topo-clusteringLinks
Fast Topological Clustering with Wasserstein Distance (ICLR 2022)
☆12Updated 3 years ago
Alternatives and similar repositories for topo-clustering
Users that are interested in topo-clustering are comparing it to the libraries listed below
Sorting:
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- Official code for Fisher information embedding for node and graph learning (ICML 2023)☆19Updated 2 years ago
- Implementation of Implicit Graphon Neural Representation☆12Updated 2 years ago
- ☆27Updated 5 months ago
- NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks☆37Updated 2 years ago
- Uncertainty Quantification over Graph with Conformalized Graph Neural Networks (NeurIPS 2023)☆82Updated last year
- Code for our ICLR19 paper "Wasserstein Barycenters for Model Ensembling", Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Cicero…☆22Updated 5 years ago
- This repository reproduces the results in the paper "How expressive are transformers in spectral domain for graphs?"(published in TMLR)☆12Updated 3 years ago
- PyTorch implementation of Pseudo-Riemannian Graph Convolutional Networks (NeurIPS'22))☆17Updated last year
- Source code for ICDM 2022 paper "Set2Box: Similarity Preserving Representation Learning for Sets."☆14Updated 2 years ago
- Official repository for the paper: "Trees with Attention for Set Prediction Tasks" (ICML21)☆10Updated 3 years ago
- Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'☆18Updated 2 years ago
- Simplicial neural network benchmarking software☆17Updated 3 years ago
- ☆18Updated 2 years ago
- Source code of "What Makes Graph Neural Networks Miscalibrated?" (NeurIPS 2022)☆24Updated 3 months ago
- Pytorch (PyG) and Tensorflow (Keras/Spektral) implementation of Total Variation Graph Neural Network (TVGNN), as presented at ICML 2023.☆20Updated 6 months ago
- Size-Invariant Graph Representations for Graph Classification Extrapolations (ICML 2021 Long Talk)☆23Updated 2 years ago
- [ICML 2024] How Interpretable Are Interpretable Graph Neural Networks?☆14Updated last year
- PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)☆24Updated 3 years ago
- [ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization☆52Updated last year
- Diffusion Models for Causal Discovery☆85Updated 2 years ago
- This is the official codebase of `Exploring Generative Neural Temporal Point Process' (Accepted by TMLR).☆20Updated 2 years ago
- Efficient Conditionally Invariant Representation Learning (ICLR 2023, Oral)☆21Updated 2 years ago
- Repository for the NeurIPS 2023 paper "Beyond Confidence: Reliable Models Should Also Consider Atypicality"☆13Updated last year
- ☆20Updated 3 years ago
- Uncertainty Aware Semi-Supervised Learning on Graph Data☆38Updated 4 years ago
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆68Updated 3 years ago
- A Pytorch implementation of missing data imputation using optimal transport.☆103Updated 4 years ago
- Official repository for Cell Attention Networks☆14Updated last year
- WWW22 - MiDaS: Representative Hypergraph Sampling☆12Updated last year