Optimal transport tools implemented with the JAX framework, to get differentiable, parallel and jit-able computations.
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Updated
Jul 7, 2024 - Python
Optimal transport tools implemented with the JAX framework, to get differentiable, parallel and jit-able computations.
TorchDR - PyTorch Dimensionality Reduction
Unified optimal transport framework for universal domain adaptation
TorchCFM: a Conditional Flow Matching library
Multi-omic single-cell optimal transport tools
A collection of AWESOME things about domian adaptation
Multimodal prototyping for cancer survival prediction - ICML 2024
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology - CVPR 2024
POT : Python Optimal Transport
Keypoint-guided Factorization for Optimal Transport
Code for the paper Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds.
Unified optimal transport framework for cross-modal retrieval
GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling
書籍『最適輸送の理論とアルゴリズム』のサポートページです。
Using Topological Sort, Shortest Path, and Regex, we tackle the second part of the smart city challenge. The first task includes using DP or Topological Sort to find an optimal schedule for the tasks. The second includes using optimal path, or Dijkstra's to find optimal paths to get around the city using walking or a train system.
Optimal transport for data recoding
Implementation of "Sparsity-Constrained Optimal Transport", ICLR 2023.
Implementation of Sinkhorn Step in JAX, NeurIPS 2023.
PyTorch implementation of slicing adversarial network (SAN)
This is an official repository for "LAVA: Data Valuation without Pre-Specified Learning Algorithms" (ICLR2023).
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