PythonOT / OTML_course_2022Links
☆10Updated 3 years ago
Alternatives and similar repositories for OTML_course_2022
Users that are interested in OTML_course_2022 are comparing it to the libraries listed below
Sorting:
- Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"☆42Updated last year
- Learning Autoencoders with Relational Regularization☆46Updated 4 years ago
- Efficient Conditionally Invariant Representation Learning (ICLR 2023, Oral)☆21Updated 2 years ago
- A list of awesome papers and cool resources on optimal transport and its applications in general! As you will notice, this list is curren…☆228Updated 4 years ago
- A list of papers for group meeting☆16Updated 3 months ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- A Pytorch implementation of missing data imputation using optimal transport.☆102Updated 4 years ago
- A new mini-batch framework for optimal transport in deep generative models, deep domain adaptation, approximate Bayesian computation, col…☆37Updated 2 years ago
- Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport☆45Updated 2 years ago
- Noise Contrastive Estimation (NCE) in PyTorch☆32Updated 5 months ago
- ☆14Updated 4 years ago
- Code for "Explainability methods for graph convolutional neural networks" - PE Pope*, S Kolouri*, M Rostami, CE Martin, H Hoffmann (CVPR …☆34Updated 5 months ago
- Code for Neural Manifold Clustering and Embedding☆61Updated 3 years ago
- ☆17Updated 3 years ago
- A curated list of papers and resources about the distribution shift in machine learning.☆120Updated 2 years ago
- Distributional Sliced-Wasserstein distance code☆50Updated last year
- Official PyTorch implementation of 🏁 MFCVAE 🏁: "Multi-Facet Clustering Variatonal Autoencoders (MFCVAE)" (NeurIPS 2021). A class of var…☆40Updated last year
- Code for Optimal Transport for structured data with application on graphs☆102Updated 2 years ago
- Code for our ICLR19 paper "Wasserstein Barycenters for Model Ensembling", Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Cicero…☆22Updated 5 years ago
- Papers and Codes for the deep learning in hyperbolic space☆179Updated 3 years ago
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆78Updated 3 years ago
- [ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization☆52Updated last year
- Disentangled gEnerative cAusal Representation (DEAR)☆61Updated 2 years ago
- Uncertainty Aware Semi-Supervised Learning on Graph Data☆38Updated 4 years ago
- Code for Sliced Gromov-Wasserstein☆69Updated 5 years ago
- Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"☆29Updated 6 years ago
- Code for ECML/PKDD paper: "LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport"☆16Updated 4 years ago
- ☆41Updated 5 years ago
- Implementation of Multi-View Information Bottleneck☆131Updated 5 years ago
- Learning Generative Models across Incomparable Spaces (ICML 2019)☆27Updated 5 years ago