About

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

You can find evaluation results in the subtasks. You can also submitting evaluation metrics for this task.

Subtasks

Datasets

Greatest papers with code

EfficientDet: Scalable and Efficient Object Detection

CVPR 2020 tensorflow/models

Model efficiency has become increasingly important in computer vision.

 Ranked #1 on Object Detection on COCO minival (AP50 metric)

AUTOML REAL-TIME OBJECT DETECTION

EfficientNetV2: Smaller Models and Faster Training

1 Apr 2021rwightman/pytorch-image-models

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 NEURAL ARCHITECTURE SEARCH

MixConv: Mixed Depthwise Convolutional Kernels

22 Jul 2019rwightman/pytorch-image-models

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 OBJECT DETECTION

Benchmarking Automatic Machine Learning Frameworks

17 Aug 2018EpistasisLab/tpot

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

AUTOMATED FEATURE ENGINEERING HYPERPARAMETER OPTIMIZATION

Layered TPOT: Speeding up Tree-based Pipeline Optimization

18 Jan 2018EpistasisLab/tpot

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

29 Jul 2016rhiever/tpot

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

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

20 Mar 2016rhiever/tpot

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 NEURAL ARCHITECTURE SEARCH

Auto-Keras: An Efficient Neural Architecture Search System

27 Jun 2018keras-team/autokeras

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

8 Jul 2020automl/auto-sklearn

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

Efficient and Robust Automated Machine Learning

NeurIPS 2015 automl/auto-sklearn

The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts.

4 HYPERPARAMETER OPTIMIZATION