110 papers with code • 0 benchmarks • 5 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

Greatest papers with code

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

google-research/google-research 6 Mar 2020

However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks---or similarly restrictive search spaces.


EfficientNetV2: Smaller Models and Faster Training

rwightman/pytorch-image-models 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.

Data Augmentation Image Classification +1

MixConv: Mixed Depthwise Convolutional Kernels

rwightman/pytorch-image-models 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.

AutoML Image Classification +1

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.

Automated Feature Engineering General Classification +1

Layered TPOT: Speeding up Tree-based Pipeline Optimization

EpistasisLab/tpot 18 Jan 2018

With the demand for machine learning increasing, so does the demand for tools which make it easier to use.

Automated Feature Engineering Hyperparameter Optimization

Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool

rhiever/tpot 29 Jul 2016

In this chapter, we present a genetic programming-based AutoML system called TPOT that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification problem.

AutoML General Classification

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

rhiever/tpot 20 Mar 2016

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts.

Automated Feature Engineering Hyperparameter Optimization +1

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.

Neural Architecture Search

Auto-Sklearn 2.0: The Next Generation

automl/auto-sklearn 8 Jul 2020

In this paper we introduce new Automated Machine Learning (AutoML) techniques motivated by our winning submission to the second ChaLearn AutoML challenge, PoSH Auto-sklearn.

AutoML Meta-Learning