A Collection of Variational Autoencoders (VAE) in PyTorch.
The open-source AI research workbench for scientific research and agent workflows. Local-first, model-agnostic desktop app with extensible skills, MCP tools and connectors, Python/R execution and traceable artifacts for reproducible research on macOS, Windows and Linux.
🔬 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.
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
A workflow language for data-driven computational pipelines
FMA: A Dataset For Music Analysis
Collection of popular and reproducible image denoising works.
☁️ 🚀 📊 📈 Evaluating state of the art in AI
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Open Science Desktop — local-first, model-agnostic AI research workbench for macOS, Windows & Linux. Open-source Claude Science desktop alternative built on Tauri + MCP + agent skills.
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.
A ready-to-fork Claude Code template for academics using LaTeX/Beamer + R. Multi-agent review, quality gates, adversarial QA, and replication protocols.
Sionna: An Open-Source Library for Research on Communication Systems
An R-focused pipeline toolkit for reproducibility and high-performance computing
Presentation-Ready Data Summary and Analytic Result Tables
High-fidelity performance metrics for generative models in PyTorch
PyCIL: A Python Toolbox for Class-Incremental Learning
Function-oriented Make-like declarative workflows for R
Code to accompany our paper Chen and Zimmermann (2020), "Open source cross-sectional asset pricing"