Popular GitHub Repositories for Reproducible

A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. All in a modern, AI-native editor.

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

Amphion (/?m?fa??n/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.

A Collection of Variational Autoencoders (VAE) in PyTorch.

MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

Collection of popular and reproducible image denoising works.

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

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

A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. ?25 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. ? Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.

A configurable, tunable, and reproducible library for CTR prediction https://fuxictr.github.io

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.

PyCIL: A Python Toolbox for Class-Incremental Learning

Experiments for understanding disentanglement in VAE latent representations

A research tool for the Iterated Prisoner's Dilemma

GPU-Jupyter: Your GPU-accelerated JupyterLab with a rich data science toolstack, TensorFlow and PyTorch for your reproducible deep learning experiments.

Collective Knowledge (CK), Collective Mind (CM) and Common Metadata eXchange (CMX): community-driven projects to facilitate collaborative and reproducible research and to learn how to run AI, ML, and other emerging workloads more efficiently and cost-effectively across diverse models, datasets, software, and hardware using MLPerf.

A complete end-to-end pipeline for LLM interpretability with sparse autoencoders (SAEs) using Llama 3.2, written in pure PyTorch and fully reproducible.

Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook

PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."