https://github.com/g4vrel/simple_ml
Simple implementation of ML papers
https://github.com/g4vrel/simple_ml
implementation implementation-of-research-paper papers pytorch
Last synced: 7 months ago
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Simple implementation of ML papers
- Host: GitHub
- URL: https://github.com/g4vrel/simple_ml
- Owner: g4vrel
- License: mit
- Created: 2023-11-29T09:03:56.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-12-27T20:48:32.000Z (9 months ago)
- Last Synced: 2025-01-08T22:38:54.202Z (9 months ago)
- Topics: implementation, implementation-of-research-paper, papers, pytorch
- Language: Python
- Homepage:
- Size: 1.77 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# simple_ml
Simple implementation of ML papers centered around generative models.
## Implemented papers
| Paper | Source | Code |
| --- | --- | --- |
| NICE: Non-linear Independent Components Estimation | [Arxiv](https://arxiv.org/abs/1410.8516) | [Code](./NICE) |
| Denoising Diffusion Probabilistic Models | [Arxiv](https://arxiv.org/abs/2006.11239) | [Code](https://github.com/g4vrel/DDPM) |
| Generative Adversarial Networks | [Arxiv](https://arxiv.org/abs/1406.2661) | [Code](./GAN) |
| Auto-Encoding Variational Bayes | [Arxiv](https://arxiv.org/abs/1312.6114) | [Code](./VAE) |
| Adversarial Autoencoders | [Arxiv](https://arxiv.org/abs/1511.05644) | [Code](https://github.com/g4vrel/AAE) |
| InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Net | [Arxiv](https://arxiv.org/abs/1606.03657) | [Code](./InfoGAN) |
| Wasserstein GAN | [Arxiv](https://arxiv.org/abs/1701.07875) | [Code](./WGAN) |
| Score-Based Generative Modeling through Stochastic Differential Equations | [Arxiv](https://arxiv.org/abs/2011.13456) | [Code](https://github.com/g4vrel/sde_ddpm) |
| Flow Matching for Generative Modeling | [Arxiv](https://arxiv.org/abs/2210.02747v2) | [Code](https://github.com/g4vrel/CFM) |