Steppy
Lightweight, Python library for fast and reproducible experimentation 🔬
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Pytorch Vae | 5,292 | 6 months ago | 48 | apache-2.0 | Python | |||||
| A Collection of Variational Autoencoders (VAE) in PyTorch. | ||||||||||
| Fma | 1,773 | a year ago | 10 | mit | Jupyter Notebook | |||||
| FMA: A Dataset For Music Analysis | ||||||||||
| Reproducible Image Denoising State Of The Art | 1,759 | 2 years ago | 4 | |||||||
| Collection of popular and reproducible image denoising works. | ||||||||||
| Bayesian Neural Networks | 1,633 | 6 months ago | 5 | mit | Jupyter Notebook | |||||
| Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more | ||||||||||
| Recsys2019_deeplearning_evaluation | 871 | 2 years ago | 1 | agpl-3.0 | Python | |||||
| This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies. | ||||||||||
| Disentangling Vae | 668 | a year ago | 7 | other | Python | |||||
| Experiments for understanding disentanglement in VAE latent representations | ||||||||||
| Pycil | 613 | 3 months ago | 3 | other | Python | |||||
| PyCIL: A Python Toolbox for Class-Incremental Learning | ||||||||||
| Sionna | 534 |  | 4 | 3 months ago | 15 | December 08, 2023 | 9 | other | Jupyter Notebook | |
| Sionna: An Open-Source Library for Next-Generation Physical Layer Research | ||||||||||
| Facil | 425 | a year ago | 9 | mit | Python | |||||
| Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines. | ||||||||||
| Steppy | 136 | 5 | 5 years ago | 16 | November 23, 2018 | 16 | mit | Python | ||
| Lightweight, Python library for fast and reproducible experimentation :microscope: | ||||||||||
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