reproducible-research
Here are 2,258 public repositories matching this topic...
A Collection of Variational Autoencoders (VAE) in PyTorch.
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Mar 21, 2025 - Python
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
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Oct 22, 2025 - Python
A DSL for data-driven computational pipelines
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Jun 18, 2026 - Groovy
FMA: A Dataset For Music Analysis
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Jan 5, 2023 - Jupyter Notebook
Collection of popular and reproducible image denoising works.
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Dec 5, 2021
☁️ 🚀 📊 📈 Evaluating state of the art in AI
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Jun 18, 2026 - Python
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
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Jul 31, 2024 - Python
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
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Oct 20, 2023 - Jupyter Notebook
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
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Jun 16, 2026 - Stata
Insight Toolkit (ITK) -- Official Repository. ITK builds on a proven, spatially-oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions.
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Jun 18, 2026 - C++
Sionna: An Open-Source Library for Research on Communication Systems
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May 19, 2026 - Jupyter Notebook
An R-focused pipeline toolkit for reproducibility and high-performance computing
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Dec 4, 2024 - R
A ready-to-fork Claude Code template for academics using LaTeX/Beamer + R. Multi-agent review, quality gates, adversarial QA, and replication protocols.
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Jun 10, 2026 - HTML
Presentation-Ready Data Summary and Analytic Result Tables
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Jun 18, 2026 - R
High-fidelity performance metrics for generative models in PyTorch
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May 11, 2026 - Python
PyCIL: A Python Toolbox for Class-Incremental Learning
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Jan 29, 2026 - Python
Function-oriented Make-like declarative workflows for R
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May 13, 2026 - R
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.
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May 25, 2023 - Python
Code to accompany our paper Chen and Zimmermann (2020), "Open source cross-sectional asset pricing"
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Oct 22, 2025 - Python
Scientific reports/literate programming for Julia
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Mar 23, 2026 - Julia
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