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Search results for jupyter notebook optimal transport
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21 search results found
Wassdistance
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153
Approximating Wasserstein distances with PyTorch
Neuraloptimaltransport
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116
PyTorch implementation of "Neural Optimal Transport" (ICLR 2023 Spotlight)
Otbook
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71
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。
Swae
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69
Implementation of the Sliced Wasserstein Autoencoders
Wasserstein2generativenetworks
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41
PyTorch implementation of "Wasserstein-2 Generative Networks" (ICLR 2021)
Wasserstein2benchmark
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19
A set of tests for evaluating large-scale algorithms for Wasserstein-2 transport maps computation.
Optimaltransportmodeling
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17
The repository contains reproducible PyTorch source code of our paper Generative Modeling with Optimal Transport Maps, ICLR 2022.
Otcs
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16
Code for "Optimal Transport-Guided Conditional Score-Based Diffusion Model (NeurIPS, 8,7,7,6)"
Machine Learning Summer Schools
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14
Curated materials for different machine learning related summer schools
Kernelneuraloptimaltransport
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14
PyTorch implementation of "Kernel Neural Optimal Transport" (ICLR 2023)
Mcopt
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13
McOpt is an algorithm that can efficiently solve multicommodity routing problems on networks
Wasserstein2barycenters
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12
PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization".
Crossdomainfaultdiagnosis
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11
Repository containing the code for the experiments and examples of my Bachelor Thesis: Cross Domain Fault Detection through Optimal Transport
Transmorph
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10
Computational framework for dataset integration
Large Scale Wasserstein Gradient Flows
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9
Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)
Learning Embeddings Into Entropic Wasserstein Spaces Ensae
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7
A thorough review of the paper "Learning Embeddings into Entropic Wasserstein Spaces" by Frogner et al. Includes a reproduction of the results on word embeddings.
Gromov Wasserstein Statistics
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7
Statistics on the space of asymmetric networks via Gromov-Wasserstein distance
N Stark
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6
N-STARK is an algorithm that can efficiently solve routing problems with time-dependent loads on networks
Conservation Laws Manifold Learning
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6
Discovering Conservation Laws using Optimal Transport and Manifold Learning
Extremalneuraloptimaltransport
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5
PyTorch implementation of "Extremal Domain Translation with Neural Optimal Transport" (NeurIPS 2023)
Brot
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5
BROT is a self-adaptation algorithm that trade offs traffic mitigation and shortest path length on transportation networks
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1-21 of 21 search results
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