PythonOT / OTML_course_2022
☆9Updated 2 years ago
Related projects: ⓘ
- A new mini-batch framework for optimal transport in deep generative models, deep domain adaptation, approximate Bayesian computation, col…☆35Updated last year
- Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"☆38Updated 10 months ago
- Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"☆27Updated 5 years ago
- PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)☆21Updated 2 years ago
- Learning Generative Models across Incomparable Spaces (ICML 2019)☆26Updated 4 years ago
- ☆17Updated 2 years ago
- Learning Autoencoders with Relational Regularization☆44Updated 3 years ago
- Entropic Optimal Transport Benchmark (NeurIPS 2023).☆18Updated 5 months ago
- A list of papers for group meeting☆15Updated last week
- ☆13Updated 4 years ago
- ☆39Updated 4 years ago
- Code for our ICLR19 paper "Wasserstein Barycenters for Model Ensembling", Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Cicero…☆20Updated 4 years ago
- Learning Graphons via Structured Gromov-Wasserstein Barycenters☆23Updated 3 years ago
- Official Python3 implementation of our ICML 2021 paper "Unbalanced minibatch Optimal Transport; applications to Domain Adaptation"☆42Updated 2 years ago
- Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport☆43Updated last year
- Implementation of Implicit Graphon Neural Representation☆12Updated last year
- ☆13Updated 2 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆30Updated 3 years ago
- Sinkhorn Barycenters via Frank-Wolfe algorithm☆24Updated 4 years ago
- ☆15Updated 2 years ago
- Experiments to reproduce results in Interventional Causal Representation Learning.☆25Updated last year
- Distributional Sliced-Wasserstein distance code☆47Updated last month
- Contains the code relative to the paper Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning https://arxiv.org/abs…☆20Updated 4 years ago
- Code for ECML/PKDD paper: "LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport"☆15Updated 3 years ago
- Official PyTorch implementation for paper: Energy-Based Sliced Wasserstein Distance☆11Updated 7 months ago
- Official Implementation of Paper "Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling" (ICML 2023)☆11Updated last year
- This repository is the implementation of Deep Dirichlet Process Mixture Models (UAI 2022)☆12Updated 2 years ago
- Noise Contrastive Estimation (NCE) in PyTorch☆30Updated 10 months ago
- GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning☆33Updated 2 years ago
- Uncertainty Aware Semi-Supervised Learning on Graph Data☆39Updated 3 years ago