POT : Python Optimal Transport
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Updated
Jun 22, 2024 - Python
POT : Python Optimal Transport
Optimal transport tools implemented with the JAX framework, to get differentiable, parallel and jit-able computations.
TorchCFM: a Conditional Flow Matching library
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Implementation of the Sliced Wasserstein Autoencoder using PyTorch
An implementation of Wasserstein Fair Classification, a conference paper submitted to UAI 2019.
GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling
CVPR 2020, Semantic Correspondence as an Optimal Transport Problem, Pytorch Implementation.
A PyTorch implementation of adaptive Monte Carlo Optimal Transport algorithm
The Wasserstein Distance and Optimal Transport Map of Gaussian Processes
Official Implementation of AlignMixup - CVPR 2022
A Python implementation of Monge optimal transportation
Multi-omic single-cell optimal transport tools
Continuous-time gradient flow for generative modeling and variational inference
Capsule research with our trivial contribution
This is an official repository for "LAVA: Data Valuation without Pre-Specified Learning Algorithms" (ICLR2023).
A Wasserstein Subsequence Kernel for Time Series.
This Python package will allow you to replicate the experiments from our research on applying Optimal Transport as a similarity metric in between single-cell omics data.
[ICLR2023] PLOT: Prompt Learning with Optimal Transport for Vision-Language Models
The code for "Point2Mask: Point-supervised Panoptic Segmentation via Optimal Transport", ICCV2023
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