AutoML

165 papers with code • 2 benchmarks • 7 datasets

Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)

Source: CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms

Libraries

Use these libraries to find AutoML models and implementations
14 papers
101
4 papers
4,716
3 papers
6,433
See all 12 libraries.

Most implemented papers

EfficientDet: Scalable and Efficient Object Detection

google/automl CVPR 2020

Model efficiency has become increasingly important in computer vision.

EfficientNetV2: Smaller Models and Faster Training

google/automl 1 Apr 2021

By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87. 3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2. 0% accuracy while training 5x-11x faster using the same computing resources.

Auto-Keras: An Efficient Neural Architecture Search System

keras-team/autokeras 27 Jun 2018

In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search.

MixConv: Mixed Depthwise Convolutional Kernels

tensorflow/tpu 22 Jul 2019

In this paper, we systematically study the impact of different kernel sizes, and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency.

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

mit-han-lab/amc ECCV 2018

Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets.

The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development

HDI-Project/BTB 22 May 2019

To address these problems, we introduce the Machine Learning Bazaar, a new framework for developing machine learning and automated machine learning software systems.

Once-for-All: Train One Network and Specialize it for Efficient Deployment

mit-han-lab/once-for-all 26 Aug 2019

On diverse edge devices, OFA consistently outperforms state-of-the-art (SOTA) NAS methods (up to 4. 0% ImageNet top1 accuracy improvement over MobileNetV3, or same accuracy but 1. 5x faster than MobileNetV3, 2. 6x faster than EfficientNet w. r. t measured latency) while reducing many orders of magnitude GPU hours and $CO_2$ emission.

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

awslabs/autogluon 13 Mar 2020

We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file.

MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements

thomas-young-2013/lite-bo 5 Dec 2020

Instead of sampling configurations randomly in HB, BOHB samples configurations based on a BO surrogate model, which is constructed with the high-fidelity measurements only.

Benchmarking Automatic Machine Learning Frameworks

EpistasisLab/tpot 17 Aug 2018

AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process.