An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
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Updated
May 10, 2024 - Python
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.
An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.
Awesome Knowledge Distillation
micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、reg…
A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility
Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。
A curated list of neural network pruning resources.
A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)
[CVPR 2023] Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs
Pytorch implementation of various Knowledge Distillation (KD) methods.
A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.
Efficient computing methods developed by Huawei Noah's Ark Lab
NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
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.
Java interface for fastText
Lightweight and Scalable framework that combines mainstream algorithms of Click-Through-Rate prediction based computational DAG, philosophy of Parameter Server and Ring-AllReduce collective communication.
Collection of recent methods on (deep) neural network compression and acceleration.
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