An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

AutoML library for deep learning

A curated list of automated machine learning papers, articles, tutorials, slides and projects

Differentiable architecture search for convolutional and recurrent networks

Fast and flexible AutoML with learning guarantees.

PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)

FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai

Automated deep learning algorithms implemented in PyTorch.

Automated Machine Learning on Kubernetes

A curated list of awesome architecture search resources

An autoML framework & toolkit for machine learning on graphs.

Fast & Simple Resource-Constrained Learning of Deep Network Structure

Genetic neural architecture search with Keras

Slimmable Networks, AutoSlim, and Beyond, ICLR 2019, and ICCV 2019

[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory

This is a list of interesting papers and projects about TinyML.

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"

a distributed Hyperband implementation on Steroids