AutoML
252 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
Benchmarks
These leaderboards are used to track progress in AutoML
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Libraries
Use these libraries to find AutoML models and implementations
NAS-Bench-101
MedMNIST
Chalearn-AutoML-1
Most implemented papers
EfficientDet: Scalable and Efficient Object Detection
Model efficiency has become increasingly important in computer vision.
EfficientNetV2: Smaller Models and Faster Training
google/automl
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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
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
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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
Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets.
Once-for-All: Train One Network and Specialize it for Efficient Deployment
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.
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
To address these problems, we introduce the Machine Learning Bazaar, a new framework for developing machine learning and automated machine learning software systems.
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
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.
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
automl/tabpfn
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We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification methods.
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
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.
