Popular GitHub Repositories for Transport

POT : Python Optimal Transport

TorchCFM: a Conditional Flow Matching library

Optimal transport tools implemented with the JAX framework, to solve large scale matching problems of any flavor.

[NeurIPS 2024] GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling

Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov

Neural Light Transport for Relighting and View Synthesis

PyTorch implementation of "Neural Optimal Transport" (ICLR 2023 Spotlight)

[CVPR 2022 Oral] Marginal Correspondence for Conditional Image Generation, [CVPR 2021] Unbalanced Feature Transport for Exemplar-based Image Translation

[ICLR2023] PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

MCPOmni Connect is a versatile command-line interface (CLI) client designed to connect to various Model Context Protocol (MCP) servers using both stdio and sse transport. It provides seamless integration with OpenAI models and all other LLM and supports dynamic tool and resource management across multiple servers.

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Play, learn, solve, and analyze No-Limit Texas Hold Em. Implementation follows from Monte Carlo counter-factual regret minimization over with hierarchical K-means imperfect recall abstractions.

Implementation of the Sliced Wasserstein Autoencoder using PyTorch

PyTorch implementation of slicing adversarial network (SAN)

Анализ трафика на круговом движении с использованием компьютерного зрения

The official repository for the paper "Optimal Flow Matching: Learning Straight Trajectories in Just One Step" (NeurIPS 2024)

Regression Graph Neural Network (regGNN) for cognitive score prediction.

PyTorch implementation of "Wasserstein-2 Generative Networks" (ICLR 2021)

ProGAN with Standard, WGAN, WGAN-GP, LSGAN, BEGAN, DRAGAN, Conditional GAN, InfoGAN, and Auxiliary Classifier GAN training methods

Thanks to the proliferation of smart devices, such as smartphones and wearables, which are equipped with computation, communication and sensing capabilities, a plethora of new location-based services and applications are available for the users at any time and everywhere. Understanding human mobility has gain importance to offer better services able to provide valuable products to the user whenever it's required. The ability to predict when and where individuals will go next allows enabling smart recommendation systems or a better organization of resources such as public transport vehicles or taxis. Network providers can predict future activities of individuals and groups to optimize network handovers, while transport systems can provide more vehicles or lines where required, reducing waiting time and discomfort to their clients. The re