#Neural-architecture-search
Showing 28 of 28 repositories tagged #neural-architecture-search, ranked by stars
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
Differentiable architecture search for convolutional and recurrent networks
Fast and flexible AutoML with learning guarantees.
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
AI on the way. An RDBMS approach to deep learning. Declarative, explainable, scalable, optimizable, easy to deploy, all that good stuff.
Automated Machine Learning on Kubernetes
This is a list of interesting papers and projects about TinyML.
[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
Early POC of genetic neural architecture search
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
A unified interface for optimization algorithms and experiments
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
Neural Architecture Search Powered by Swarm Intelligence 🐜
Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)
Single Path One-Shot NAS MXNet implementation with full training and searching pipeline. Support both Block and Channel Selection. Searched models better than the original paper are provided.
A toolbox for receptive field analysis and visualizing neural network architectures
A paper collection about automated graph learning
Neural Pipeline Search (NePS): Helps deep learning experts find the best neural pipeline.
Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
my own deep learning mastery roadmap
TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation
Minimal Tensorflow implementation of the paper "Neural Architecture Search With Reinforcement Learning" presented at ICLR 2017
The Cerebros package is an ultra-precise Neural Architecture Search (NAS) / AutoML that is intended to much more closely mimic biological neurons than conventional neural network architecture strategies.