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https://github.com/layer6ai-labs/CEF
Code for the NeurIPS'21 paper "Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows"
https://github.com/layer6ai-labs/CEF
Last synced: 3 months ago
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Code for the NeurIPS'21 paper "Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows"
- Host: GitHub
- URL: https://github.com/layer6ai-labs/CEF
- Owner: layer6ai-labs
- License: mit
- Created: 2021-10-26T14:56:01.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2021-11-10T15:52:29.000Z (about 3 years ago)
- Last Synced: 2024-07-03T23:44:49.247Z (4 months ago)
- Language: Python
- Homepage:
- Size: 736 KB
- Stars: 6
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
NeurIPS'21 Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows
=====
This repo contains the code for the paper [Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows](https://openreview.net/forum?id=DqU-rIHy4Eh) (NeurIPS 2021). See the `experiments` folder for usage.
This code is derived from the [`nflows`](https://github.com/bayesiains/nflows) repo:
```
Conor Durkan, Artur Bekasov, Iain Murray, & George Papamakarios. (2020). nflows: normalizing flows in PyTorch (v0.14). Zenodo. https://doi.org/10.5281/zenodo.4296287
```