awesome-AutoML

AutoML resource collection

A curated list of AutoML tools, research, projects and resources to aid in the development and exploration of automated machine learning systems.

Curating a list of AutoML-related research, tools, projects and other resources

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AutoML / Research papers / AutoML survey
Neural architecture search: a survey 深度神经网络结构搜索综述 (Tang et al. 2021)
AutoML to Date and Beyond: Challenges and Opportunities (Santu et al. 2020)
A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions (Ren et al. 2020)
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice (Yang et al. 2020)
Benchmark and Survey of Automated Machine Learning Frameworks (Zoller et al. 2019)
AutoML: A Survey of the State-of-the-Art (He et al. 2019)
A Survey on Neural Architecture Search (Wistuba et al. 2019)
Neural Architecture Search: A Survey (Elsken et al. 2019)
Taking Human out of Learning Applications: A Survey on Automated Machine Learning (Yao et al. 2018)
Archon: An Architecture Search Framework for Inference-Time Techniques (Saad-Falcon. 2024)
LayerNAS: Neural Architecture Search in Polynomial Complexity (Fan et al. 2023)
EvoPrompting: Language Models for Code-Level Neural Architecture Search (Chen et al. 2023)
Neural Architecture Search using Property Guided Synthesis (Jin et al. 2022)
Data-Free Neural Architecture Search via Recursive Label Calibration (Liu et al. 2022)
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs (Akin et al. 2022)
Resource-Constrained Neural Architecture Search on Tabular Datasets (Yang et al. 2022)
Searching for Fast Model Families on Datacenter Accelerators (Li et al. 2022)
Towards the co-design of neural networks and accelerators (Zhou et al. 2022)
Neural Architecture Search for Energy Efficient Always-on Audio Models (Speckhard et al. 2022)
KNAS: Green Neural Architecture Search (Xu et al. 2021)
Primer: Searching for Efficient Transformers for Language Modeling (So et al. 2021)
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search (Xu et al. 2021)
Accelerating Neural Architecture Search for Natural Language Processing with Knowledge Distillation and Earth Mover's Distance (Li et al. 2021)
AlphaNet: Improved Training of Supernets with Alpha-Divergence (Wang et al. 2021)
AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling (Wang et al. 2021)
Speedy Performance Estimation for Neural Architecture Search (Ru et al. 2021)
AutoFormer: Searching Transformers for Visual Recognition (Chen et al. 2021)
NAAS: Neural Accelerator Architecture Search (Lin et al. 2021)
ModularNAS: Towards Modularized and Reusable Neural Architecture Search (Lin et al. 2021)
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search (Li et al. 2021)
AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression (Zhu et al. 2021)
AutoSpace: Neural Architecture Search with Less Human Interference (Zhou et al. 2021)
ReNAS:Relativistic Evaluation of Neural Architecture Search (Xu et al. 2021)
Searching for Fast Model Families on Datacenter Accelerators (Li et al. 2021)
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition (Lin et al. 2021)
PyGlove: Symbolic Programming for Automated Machine Learning (Peng et al. 2021)
DARTS-: Robustly Stepping out of Performance Collapse Without Indicators (Chu et al. 2021)
NAS-DIP: Learning Deep Image Prior with Neural Architecture Search (Chen et al. 2020)
AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification (Wang et al. 2020)
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending (Xu et al. 2020)
Few-shot Neural Architecture Search (Zhao et al. 2020)
Efficient Neural Architecture Search via Proximal Iterations (Yao et al. 2020)
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search (Peng et al. 2020)
How Does Supernet Help in Neural Architecture Search? (Zhang et al. 2020)
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending (Xu et al. 2020)
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy (Wang et al. 2020)
MCUNet: Tiny Deep Learning on IoT Devices (Lin et al. 2020)
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions (Wan et al. 2020)
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators (Xiong et al. 2020)
Neural Architecture Transfer (Lu et al. 2020)
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy (Wang et al. 2020)
When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks (Guo et al. 2020)
Semi-Supervised Neural Architecture Search (Luo et al. 2020)
MixPath: A Unified Approach for One-shot Neural Architecture Search (Chu et al. 2020)
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (Real et al. 2020)
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data (Such et al. 2019)
CARS: Continuous Evolution for Efficient Neural Architecture Search (Yang et al. 2019)
Meta-Learning of Neural Architectures for Few-Shot Learning (Elsken et al. 2019)
Up to two billion times acceleration of scientific simulations with deep neural architecture search (Kasim et al. 2019)
Efficient Forward Architecture Search (Hue et al. 2019)
Towards Oracle Knowledge Distillation with Neural Architecture Search (Kang et al. 2019)
Blockwisely Supervised Neural Architecture Search with Knowledge Distillation (Li et al. 2019)
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection (Ghiasi et al. 2019)
Improving Keyword Spotting and Language Identification via Neural Architecture Search at Scale (Mazzawi et al. 2019)
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization (Du et al. 2019)
Efficient Neural Interaction Function Search for Collaborative Filtering (Yao et al. 2019)
Evaluating the Search Phase of Neural Architecture Search (Sciuto et al. 2019)
MixConv: Mixed Depthwise Convolutional Kernels (Tan et al. 2019)
Multinomial Distribution Learning for Effective Neural Architecture Search (Zheng et al. 2019)
SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-task Learning (Ma et al. 2019)
PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search (Xu et al. 2019) -
Single Path One-Shot Neural Architecture Search with Uniform Sampling (Guo et al. 2019)
AutoGAN: Neural Architecture Search for Generative Adversarial Networks (Gong et al. 2019)
MixConv: Mixed Depthwise Convolutional Kernels (Tan et al. 2019)
Tiny Video Networks (Piergiovanni et al. 2019)
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures (Ryoo et al. 2019)
EfficientNet-EdgeTPU: Creating Accelerator-Optimized Neural Networks with AutoML (Gupta et al. 2019)
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (Tan et al. 2019)
MoGA: Searching Beyond MobileNetV3 (Chu et al. 2019) -
Searching for MobileNetV3 (Howard et al. 2019)
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation (Liu et al. 2019)
DetNAS: Backbone Search for Object Detection (Chen et al. 2019)
Graph HyperNetworks for Neural Architecture Search (Zhang et al. 2019)
Dynamic Distribution Pruning for Efficient Network Architecture Search (Zheng et al. 2019)
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search (Chu et al. 2019)
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers (Fedorov et al. 2019)
EENA: Efficient Evolution of Neural Architecture (Zhu et al. 2019)
Single Path One-Shot Neural Architecture Search with Uniform Sampling (Guo et al. 2019)
InstaNAS: Instance-aware Neural Architecture Search (Cheng et al. 2019)
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware (Cai et al. 2019)
NAS-Bench-101: Towards Reproducible Neural Architecture Search (Ying et al. 2019)
Evolutionary Neural AutoML for Deep Learning (Liang et al. 2019)
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search (Chu et al. 2019)
The Evolved Transformer (So et al. 2019)
SNAS: Stochastic Neural Architecture Search (Xie et al. 2019)
NeuNetS: An Automated Synthesis Engine for Neural Network Design (Sood et al. 2019)
EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search (Fang et al. 2019)
Understanding and Simplifying One-Shot Architecture Search (Bender et al. 2018)
Evolving Space-Time Neural Architectures for Videos (Piergiovanni et al. 2018)
IRLAS: Inverse Reinforcement Learning for Architecture Search (Guo et al. 2018)
Neural Architecture Search with Bayesian Optimisation and Optimal Transport (Kandasamy et al. 2018)
Path-Level Network Transformation for Efficient Architecture Search (Cai et al. 2018)
BlockQNN: Efficient Block-wise Neural Network Architecture Generation (Zhong et al. 2018)
Stochastic Adaptive Neural Architecture Search for Keyword Spotting (Véniat et al. 2018)
Task-Driven Convolutional Recurrent Models of the Visual System (Nayebi et al. 2018)
Neural Architecture Optimization (Luo et al. 2018)
MnasNet: Platform-Aware Neural Architecture Search for Mobile (Tan et al. 2018)
Neural Architecture Search: A Survey (Elsken et al. 2018)
MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning (Hsu et al. 2018)
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications (Yang et al. 2018)
Auto-Meta: Automated Gradient Based Meta Learner Search (Kim et al. 2018)
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks (Gordon et al. 2018)
DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures (Dong et al. 2018)
Searching Toward Pareto-Optimal Device-Aware Neural Architectures (Cheng et al. 2018)
Differentiable Architecture Search (Liu et al. 2018)
Regularized Evolution for Image Classifier Architecture Search (Real et al. 2018)
Efficient Architecture Search by Network Transformation (Cai et al. 2017)
Large-Scale Evolution of Image Classifiers (Real et al. 2017)
Progressive Neural Architecture Search (Liu et al. 2017)
AdaNet: Adaptive Structural Learning of Artificial Neural Networks (Cortes et al. 2017)
Learning Transferable Architectures for Scalable Image Recognition (Zoph et al. 2017)
Federated Neural Architecture Search (Xu et al 2020)
Direct Federated Neural Architecture Search (Garg et al 2020)

AutoML / Research papers / Neural Architecture Search benchmark
NAS-Bench-101: Towards Reproducible Neural Architecture Search (Ying et al. 2019) -
Symbolic Discovery of Optimization Algorithms (Chen et al. 2017)
Neural Optimizer Search with Reinforcement Learning (Bello et al. 2017)
Searching for Activation Functions (Ramachandran et al. 2017)

AutoML / Research papers / AutoAugment
MetaAugment: Sample-Aware Data Augmentation Policy Learning (Zhou et al. 2020)
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition (Park et al. 2019)
RandAugment: Practical automated data augmentation with a reduced search space (Cubuk et al. 2019)
Learning Data Augmentation Strategies for Object Detection (Zoph et al. 2019)
Fast AutoAugment (Lim et al. 2019)
AutoAugment: Learning Augmentation Policies from Data (Cubuk et al. 2018)

AutoML / Research papers / AutoDropout
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks (Pham et al. 2020)

AutoML / Research papers / AutoDistill
AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models (Zhang et al. 2022)

AutoML / Research papers / Learning to learn/Meta-learning
ES-MAML: Simple Hessian-Free Meta Learning (Song et al. 2019)
Learning to Learn with Gradients (Chelsea Finn PhD disseration 2018)
On First-Order Meta-Learning Algorithms (OpenAI Reptile by Nichol et al. 2018)
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (MAML by Finn et al. 2017)
A sample neural attentive meta-learner (Mishra et al. 2017)
Learning to Learn without Gradient Descent by Gradient Descent (Chen et al. 2016)
Learning to learn by gradient descent by gradient descent (Andrychowicz et al. 2016)
Learning to reinforcement learn (Wang et al. 2016)
RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning (Duan et al. 2016)

AutoML / Research papers / Hyperparameter optimization
OptFormer: Towards Universal Hyperparameter Optimization with Transformers (Chen et al. 2022)
Frugal Optimization for Cost-related Hyperparameters (Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021)
Economical Hyperparameter Optimization With Blended Search Strategy (Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021)
ChaCha for Online AutoML (Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. ICML 2021)
Using a thousand optimization tasks to learn hyperparameter search strategies (Metz et al. 2020)
AutoNE: Hyperparameter Optimization for Massive Network Embedding (Tu et al. 2019)
Population Based Training of Neural Networks (Jaderberg et al. 2017)
Google Vizier: A Service for Black-Box Optimization (Golovin et al. 2017)
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization (Li et al. 2016)
Practical Bayesian Optimization of Machine Learning Algorithms (Snoek et al. 2012)
Random Search for Hyper-Parameter Optimization (Bergstra et al. 2012)

AutoML / Research papers / Automatic feature selection
Deep Feature Synthesis: Towards Automating Data Science Endeavors (Kanter et al. 2017)
ExploreKit: Automatic Feature Generation and Selection (Katz et al. 2016)

AutoML / Research papers / Recommendation systems
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale (Wen et al. 2023)
AutoML for Deep Recommender Systems: A Survey (Zheng et al. 2022)
Automated Machine Learning for Deep Recommender Systems: A Survey (Chen et al. 2022)
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations (Zhao et al. 2021)
AutoDim: Field-aware Embedding Dimension Searchin Recommender Systems (Zhao et al. 2021)
Learnable Embedding Sizes for Recommender Systems (Liu et al. 2021)
AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System (Zhao et al. 2021)
AIM: Automatic Interaction Machine for Click-Through Rate Prediction (Zhu et al. 2021)
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction (Song et al. 2020)
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction (Liu et al. 2020)
AutoFeature: Searching for Feature Interactions and Their Architectures for Click-through Rate Prediction (Khawar et al. 2020)
AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction (Liu et al. 2020)
Neural Input Search for Large Scale Recommendation Models (Joglekar et al. 2019)

AutoML / Research papers / Model compression
AMC: AutoML for Model Compression and Acceleration on Mobile Devices (He et al. 2018)

AutoML / Research papers / Quantization
HAQ: Hardware-Aware Automated Quantization with Mixed Precision (Wang et al. 2018)

AutoML / Research papers / Tech to speech
LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search (Luo et al. 2021)

AutoML / Research papers / Bandits
AutoML for Contextual Bandits (Dutta et al. 2019)

AutoML / Research papers / Reinforcement learning
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems (Parker-Holder et al. 2022)
Designing Neural Network Architectures using Reinforcement Learning (Baker et al. 2016)
Neural Architecture Search with Reinforcement Learning (Zoph and Le. 2016)

AutoML / Research papers / Graph neural network
AutoGL: A Library for Automated Graph Learning (Guan et al. 2021)
AutoGraph: Automated Graph Neural Network (Li et al. 2020)

AutoML / Research papers / Quantum computing
QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits (Wang et al. 2022)
Differentiable Quantum Architecture Search (Zhang et al. 2020)
Large Language Models Are Human-Level Prompt Engineers (Zhou et al. 2022)
Neural Prompt Search (Zhang et al. 2022)
AUTOPROMPT: Eliciting Knowledge from Language Models with Automatically Generated Prompts (Shin et al. 2020)

AutoML / Research papers / LLM
LLaMA-NAS: Efficient Neural Architecture Search for Large Language Models (Sarah et al. 2024)
15 times Faster than Llama 2: Introducing DeciLM – NAS-Generated LLM with Variable GQA (Deci Research 2023)

AutoML / Tools and projects
Falcon 159 8 months ago : A Lightweight AutoML Library
MindWare 52 about 3 years ago : Efficient Open-source AutoML System
AutoDL 1,140 about 2 years ago : automated deep learning
AutoGL 1,091 4 months ago : An autoML framework & toolkit for machine learning on graphs
MLBox 1,500 over 1 year ago : a powerful Automated Machine Learning python library
FLAML 3,938 13 days ago : Fast and lightweight AutoML ( )
Hypernets 267 5 months ago : A General Automated Machine Learning Framework
Cooka 40 about 1 year ago : a lightweight and visualization toolkit
Vegas 848 almost 2 years ago : an AutoML algorithm tool chain by Huawei Noah's Arb Lab
TransmogrifAI 2,248 about 1 year ago : an AutoML library written in Scala that runs on top of Apache Spark
Model Search 3,269 4 months ago : a framework that implements AutoML algorithms for model architecture search at scale
AutoGluon : AutoML Toolkit for Deep Learning
hyperunity 136 almost 5 years ago : A toolset for black-box hyperparameter optimisation
auptimizer 200 almost 2 years ago : An automatic ML model optimization tool
Keras Tuner 2,858 2 days ago : Hyperparameter tuning for humans
Torchmeta 1,993 over 1 year ago : A Meta-Learning library for PyTorch
learn2learn 2,676 6 months ago : PyTorch Meta-learning Framework for Researchers
Auto-PyTorch 2,381 8 months ago : Automatic architecture search and hyperparameter optimization for PyTorch
ATM: Auto Tune Models : A multi-tenant, multi-data system for automated machine learning (model selection and tuning)
Adanet: Fast and flexible AutoML with learning guarantees 3,469 about 1 year ago : Tensorflow package for AdaNet
Microsoft Neural Network Intelligence (NNI) 14,067 5 months ago : An open source AutoML toolkit for neural architecture search and hyper-parameter tuning
Dragonfly 859 over 1 year ago : An open source python library for scalable Bayesian optimisation
H2O AutoML : Automatic Machine Learning by H2O.ai
Kubernetes Katib 1,510 29 days ago : hyperparameter Tuning on Kubernetes inspired by Google Vizier
Ray Tune : Scalable Hyperparameter Tuning¶
TransmogrifAI : automated machine learning for structured data by Salesforce
Advisor 1,548 about 5 years ago : open-source implementation of Google Vizier for hyper parameters tuning
AutoKeras : AutoML library by Texas A&M University using Bayesian optimization
AutoSklearn : an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator
Ludwig 11,219 about 20 hours ago : a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code
AutoWeka : hyperparameter search for Weka
automl-gs 1,854 about 5 years ago : Provide an input CSV and a target field to predict, generate a model + code to run it
SMAC 1,092 4 days ago : Sequential Model-based Algorithm Configuration
Hyperopt-sklearn 1,590 6 months ago : hyper-parameter optimization for sklearn
Spearmint 1,549 almost 5 years ago : a software package to perform Bayesian optimization
TPOT : one of the very first AutoML methods and open-source software packages
MOE 1,308 over 1 year ago : a global, black box optimization engine for real world metric optimization by Yelp
Hyperband 594 over 6 years ago : open source code for tuning hyperparams with Hyperband
Optuna : define-by-run hypterparameter optimization framework
RoBO 483 over 5 years ago : a Robust Bayesian Optimization framework
HpBandSter 611 about 2 years ago : a framework for distributed hyperparameter optimization
HPOlib2 139 6 months ago : a library for hyperparameter optimization and black box optimization benchmarks
Hyperopt : distributed Asynchronous Hyperparameter Optimization in Python
REMBO 113 over 11 years ago : Bayesian optimization in high-dimensions via random embedding
ExploreKit : a framework for automated feature generation
FeatureTools 7,283 about 22 hours ago : An open source python framework for automated feature engineering
EvalML 785 6 days ago : An open source python library for AutoML
PocketFlow 2,787 over 1 year ago : use AutoML to do model compression (open sourced by Tencent)
DEvol (DeepEvolution) 951 over 1 year ago : a basic proof of concept for genetic architecture search in Keras
mljar-supervised 3,068 7 days ago : AutoML with explanations and markdown reports
Determined 3,048 5 days ago : scalable deep learning training platform with integrated hyperparameter tuning support; includes Hyperband, PBT, and other search methods
AutoGL 1,091 4 months ago : an autoML framework & toolkit for machine learning on graphs)
FEDOT 649 5 days ago : AutoML framework for the design of composite pipelines
NASGym 29 over 4 years ago : a proof-of-concept OpenAI Gym environment for Neural Architecture Search (NAS)
Archai 467 about 1 month ago : a platform for Neural Network Search (NAS) that allows you to generate efficient deep networks for your applications
autoBOT 10 over 2 years ago : An autoML system for automated text classification exploiting representation evolution
autoai 175 11 days ago : A framework to find the best performing AI/ML model for any AI problem

AutoML / Benchmarks
OpenML AutoML benchmarking framework

AutoML / Commercial products
deci.ai AutoNAC : Automated Neural Architecture Construction (AutoNAC™)
Databricks AutoML : Augment experts. Empower citizen data scientists
Abacus.AI : Effortlessly Embed Cutting-Edge AI Into Your Apps
Syne Tune 393 6 days ago : state-of-the-art distributed hyperparameter optimizers (HPO)
Amazon SageMaker AutoPilot
Google Cloud AutoML
Google Cloud ML Hyperparameter Turning
Microsoft Azure Machine Learning Studio
comet.ml
SigOpt
mljar.com
Weights and Biases
Qeexo AutoML

AutoML / Blog posts
YOLO-NAS by Deci Achieves State-of-the-Art Performance on Object Detection Using Neural Architecture Search
Efficient Multi-Objective Neural Architecture Search with Ax
AutoML Solutions: What I Like and Don’t Like About AutoML as a Data Scientist
Improved On-Device ML on Pixel 6, with Neural Architecture Search
Neural Architecture Search
How we use AutoML, Multi-task learning and Multi-tower models for Pinterest Ads
A Conversation With Quoc Le: The AI Expert Behind Google AutoML
fast.ai: An Opinionated Introduction to AutoML and Neural Architecture Search
Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees
Using Evolutionary AutoML to Discover Neural Network Architectures
Improving Deep Learning Performance with AutoAugment
AutoML for large scale image classification and object detection
Using Machine Learning to Discover Neural Network Optimizers
Using Machine Learning to Explore Neural Network Architecture
Machine Learning Hyperparameter Optimization with Argo

AutoML / Courses
EfficientML.ai Lecture 7 - Neural Architecture Search (Part I) (MIT 6.5940, Fall 2023)
https://www.youtube.com/watch?v=EFpGQoDQ7JI [EfficientML.ai Lecture 8 - Neural Architecture Search (Part II) (MIT 6.5940, Fall 2023)( )

AutoML / Presentations
ICML 2019 Tutorial: Recent Advances in Population-Based Search for Deep Neural Networks by Evolving AI Lab
Automatic Machine Learning by Frank Hutter and Joaquin Vanschoren
Advanced Machine Learning Day 3: Neural Architecture Search by Debadeepta Dey (MSR)
Neural Architecture Search by Quoc Le (Google Brain)
AutoML Showdown: Google vs Amazon vs Microsoft by Roboflow

AutoML / Books
AUTOML: METHODS, SYSTEMS, CHALLENGES
Hands-On Meta Learning with Python: Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow -
Automated Machine Learning in Action A book that introduces autoML with AutoKreas and Keras Tuner

AutoML / Competitions, workshops and conferences
AutoML-Conf 2022
NIPS 2018 3rd AutoML Challenge: AutoML for Lifelong Machine Learning
AutoML Workshop in ICML

AutoML / Other curated resources on AutoML
Literature on Neural Architecture Search
Awesome-AutoML-Papers 4,028 6 months ago

Practical applications
AutoML: Automating the design of machine learning models for autonomous driving by Waymo

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