Popular GitHub Repositories for Reproducibility Crisis

? Data Versioning and ML Experiments

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ???

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

:cloud: :rocket: :bar_chart: :chart_with_upwards_trend: Evaluating state of the art in AI

The collaboration workspace for Machine Learning

High-fidelity performance metrics for generative models in PyTorch

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.

[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.

Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

Providing reproducibility in deep learning frameworks

AgentLab: An open-source framework for developing, testing, and benchmarking web agents on diverse tasks, designed for scalability and reproducibility.

Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.

Automatic Ontology and Knowledge Graph construction with LLM

Flexible and scalable template based on PyTorch Lightning + Hydra. Efficient workflow and reproducibility for rapid ML experiments.

Deep Reinforcement Learning (DRL) agents applied to medical images

The repository provides links to collections of influential and interesting research papers from top AI conferences, with open-source code to promote reproducibility and provide detailed implementation insights beyond the scope of the article. Stay up to date with the latest advances in AI research!

ForML - A development framework and MLOps platform for the lifecycle management of data science projects

A template for starting reproducible Python machine-learning projects with hardware acceleration. Find an example at https://github.com/CLAIRE-Labo/no-representation-no-trust

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