Nbis_project_template
Personal template for making and describing how to do reproducible NBIS support projects
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Sacred | 4,152 |  | 77 | 33 | 3 months ago | 29 | November 13, 2023 | 97 | mit | Python |
| Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA. | ||||||||||
| Evalai | 1,621 | 4 months ago | 322 | other | Python | |||||
| :cloud: :rocket: :bar_chart: :chart_with_upwards_trend: Evaluating state of the art in AI | ||||||||||
| Benchmark_vae | 1,597 | 4 months ago | 12 | September 06, 2023 | 21 | apache-2.0 | Python | |||
| Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022) | ||||||||||
| Drake | 1,332 | 15 | 1 | 6 months ago | 47 | November 06, 2023 | gpl-3.0 | R | ||
| An R-focused pipeline toolkit for reproducibility and high-performance computing | ||||||||||
| Gtsummary | 892 | 1 | 14 | 4 months ago | 29 | July 15, 2023 | 25 | other | R | |
| Presentation-Ready Data Summary and Analytic Result Tables | ||||||||||
| 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. | ||||||||||
| Targets | 860 | a month ago | other | R | ||||||
| Function-oriented Make-like declarative workflows for R | ||||||||||
| Torch Fidelity | 820 |  | 8 | 3 months ago | 3 | June 15, 2021 | 1 | other | Python | |
| High-fidelity performance metrics for generative models in PyTorch | ||||||||||
| Rrtools | 619 | a year ago | 12 | other | R | |||||
| rrtools: Tools for Writing Reproducible Research in R | ||||||||||
| Labnotebook | 528 | 6 years ago | 4 | mit | Jupyter Notebook | |||||
| LabNotebook is a tool that allows you to flexibly monitor, record, save, and query all your machine learning experiments. | ||||||||||
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