作者：FedAI联邦学习
链接：https://www.zhihu.com/question/334021426/answer/840727058
来源：知乎
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AutoML技术有很多种，包括:
如果有机器学习和深度学习经验的话，下边这些“干货”就可以直接入手了，咱们挨着说：
AutoML调研
1.A Survey on Neural Architecture Search (Wistuba et al. 2019)
A Survey on Neural Architecture Search | Request PDFwww.researchgate.net
2.Neural Architecture Search: A Survey (Elsken et al. 2019)
Neural Architecture Search: A Surveyarxiv.org
3.Taking Human out of Learning Applications: A Survey on Automated Machine Learning (Yao et al. 2018)
https://arxiv.org/abs/1810.13306v1arxiv.org
神经网络架构搜索
1.Neural Architecture Search with Reinforcement Learning (Zoph and Le. 2016)
Neural Architecture Search with Reinforcement Learningxueshu.baidu.com
2. Learning Transferable Architectures for Scalable Image Recognition (Zoph et al. 2017)
Learning Transferable Architectures for Scalable Image Recognitionarxiv.org
3. AdaNet: Adaptive Structural Learning of Artificial Neural Networks (Cortes et al. 2017)
AdaNet: Adaptive Structural Learning of Artificial Neural Networksxueshu.baidu.com
4.Regularized Evolution for Image Classifier Architecture Search (Real et al. 2018)
Regularized Evolution for Image Classifier Architecture Searchxueshu.baidu.com
5. Differentiable Architecture Search (Liu et al. 2018)
DARTS: Differentiable Architecture Searcharxiv.org
6.MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks (Gordon et al. 2018)
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networksxueshu.baidu.com
7.MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning (Hsu et al. 2018)
MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learningarxiv.org
8.Neural Architecture Search: A Survey (Elsken et al. 2018)
Neural Architecture Search: A Surveyarxiv.org
9.Task-Driven Convolutional Recurrent Models of the Visual System (Nayebi et al. 2018)
Task-Driven Convolutional Recurrent Models of the Visual Systemxueshu.baidu.com
10.Stochastic Adaptive Neural Architecture Search for Keyword Spotting (Véniat et al. 2018)
Stochastic Adaptive Neural Architecture Search for Keyword Spottingxueshu.baidu.com
11.IRLAS: Inverse Reinforcement Learning for Architecture Search (Guo et al. 2018)
IRLAS: Inverse Reinforcement Learning for Architecture Searchxueshu.baidu.com
12.NeuNetS: An Automated Synthesis Engine for Neural Network Design (Sood et al. 2019)
NeuNetS: An Automated Synthesis Engine for Neural Network Designxueshu.baidu.com
13.The Evolved Transformer (So et al. 2019)
The Evolved Transformerarxiv.org
14.Evolutionary Neural AutoML for Deep Learning (Liang et al. 2019)
Evolutionary Neural AutoML for Deep Learningxueshu.baidu.com
15.InstaNAS: Instance-aware Neural Architecture Search (Cheng et al. 2019)
InstaNAS: Instance-aware Neural Architecture Searchxueshu.baidu.com
16.The Evolved Transformer (So et al. 2019)
The Evolved Transformerarxiv.org
17.Single Path One-Shot Neural Architecture Search with Uniform Sampling (Guo et al. 2019)
Single Path One-Shot Neural Architecture Search with Uniform Samplingxueshu.baidu.com
18.MixConv: Mixed Depthwise Convolutional Kernels (Tan et al. 2019)
MixConv: Mixed Depthwise Convolutional Kernelsarxiv.org
19.DetNAS: Backbone Search for Object Detection (Chen et al. 2019)
DetNAS: Backbone Search for Object Detectionarxiv.org
优化器搜索
1.Neural Optimizer Search with Reinforcement Learning (Bello et al. 2017)
https://arxiv.org/abs/1709.07417arxiv.org
自动添加
1.Learning Data Augmentation Strategies for Object Detection (Zoph et al. 2019)
Learning Data Augmentation Strategies for Object Detectionarxiv.org
2.Fast AutoAugment (Lim et al. 2019)
元学习
1.Learning to Learn with Gradients (Chelsea Finn PhD disseration 2018)
Learning to Learn with Gradientsescholarship.org
2.On First-Order Meta-Learning Algorithms (OpenAI Reptile by Nichol et al. 2018)
On First-Order Meta-Learning Algorithmsxueshu.baidu.com
3.Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (MAML by Finn et al. 2017)
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networksarxiv.org
4.A simple neural attentive meta-learner (Mishra et al. 2017)
https://arxiv.org/abs/1707.03141arxiv.org
超参数优化
1.Google Vizier: A Service for Black-Box Optimization (Golovin et al. 2017)
2.Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization (Li et al. 2016)
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationarxiv.org
模型压缩
1.AMC: AutoML for Model Compression and Acceleration on Mobile Devices (He et al. 2018)
AMC: AutoML for Model Compression and Acceleration on Mobile Devicesarxiv.org
1.ATM: Auto Tune Models: A multi-tenant, multi-data system for automated machine learning (model selection and tuning)
2.Adanet: Fast and flexible AutoML with learning guarantees: Tensorflow package for AdaNet
3.Microsoft Neural Network Intelligence (NNI): An open source AutoML toolkit for neural architecture search and hyper-parameter tuning
4.Dragonfly: An open source python library for scalable Bayesian optimisation
5.H2O AutoML: Automatic Machine Learning by H2O.ai
6. Kubernetes Katib: hyperparameter Tuning on Kubernetes inspired by Google Vizier
7.TransmogrifAI: automated machine learning for structured data by Salesforce
8.AutoSklearn: an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator
9.Ludwig: a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code
10.AutoWeka: hyperparameter search for Weka
11.SMAC: Sequential Model-based Algorithm Configuration
12.Hyperopt-sklearn: hyper-parameter optimization for sklearn
13.Spearmint: a software package to perform Bayesian optimization
14.TOPT: one of the very first AutoML methods and open-source software packages
15.MOE: a global, black box optimization engine for real world metric optimization by Yelp
16.Optuna: define-by-run hypterparameter optimization framework
17.RoBO: a Robust Bayesian Optimization framework
18.HPOlib2: a library for hyperparameter optimization and black box optimization benchmarks
19.Hyperopt: distributed Asynchronous Hyperparameter Optimization in Python
20.ExploreKit: a framework forautomated feature generation
21.FeatureTools: An open source python framework for automated feature engineering
22.PocketFlow: use AutoML to do model compression (open sourced by Tencent)
23.DEvol (DeepEvolution): a basic proof of concept for genetic architecture search in Keras
1.Amazon SageMaker
2.Google Cloud AutoML
3.Google Cloud ML Hyperparameter Turning
4.Microsoft Azure Machine Learning Studio
5.SigOpt
1.ICML 2019 Tutorial: Recent Advances in Population-Based Search for Deep Neural Networks by Evolving AI Lab
2.Automatic Machine Learning by Frank Hutter and Joaquin Vanschoren
3.Advanced Machine Learning Day 3: Neural Architecture Search by Debadeepta Dey (MSR)
4.Neural Architecture Search by Quoc Le (Google Brain)
1.AUTOML: METHODS, SYSTEMS, CHALLENGES
AutoML: Methods, Systems, Challenges (new book)www.automl.org
2.NIPS 2018 3rd AutoML Challenge: AutoML for Lifelong Machine Learning
1.AutoML: Automating the design of machine learning models for autonomous driving by Waymo
英：
中：
AutoML：无人驾驶机器学习模型设计自动化 - 云+社区 - 腾讯云cloud.tencent.com
以上，如果对各位有此意向的朋友有用记得点个赞同，感谢！
