From 9367e491e7aad82dae4384c659b9a54f44fe2d8f Mon Sep 17 00:00:00 2001 From: Cedric Ewen <61949473+ewencedr@users.noreply.github.com> Date: Wed, 4 Oct 2023 11:37:09 +0200 Subject: [PATCH] add arxiv links to readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 64b474b..d2574c8 100644 --- a/README.md +++ b/README.md @@ -9,14 +9,14 @@ [![black](https://img.shields.io/badge/Code%20Style-Black-black.svg?labelColor=gray)](https://black.readthedocs.io/en/stable/) [![isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
Template -[![Paper](http://img.shields.io/badge/paper-arxiv.1001.2234-B31B1B.svg)](https://www.nature.com/articles/nature14539) +[![Paper](http://img.shields.io/badge/paper-arxiv.2310.00049-B31B1B.svg)](https://arxiv.org/abs/2310.00049) [![Conference](http://img.shields.io/badge/AnyConference-year-4b44ce.svg)](https://papers.nips.cc/paper/2020) ## Description -This is the official repository implementing the EPiC Flow Matching point cloud generative machine learning models from arxiv1111.11111. +This is the official repository implementing the EPiC Flow Matching point cloud generative machine learning models from [arxiv.2310.00049](https://arxiv.org/abs/2310.00049). EPiC Flow Matching is a [Continuous Normalising Flow](https://arxiv.org/abs/1806.07366) that is trained with a simulation free approach called [Flow Matching](https://arxiv.org/abs/2210.02747). The model uses [DeepSet](https://arxiv.org/abs/1703.06114) based [EPiC layers](https://arxiv.org/abs/2301.08128) for the architecture, which allow for good scalability to high set sizes.