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)
AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process.
With the demand for machine learning increasing, so does the demand for tools which make it easier to use.
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.
In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search.
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.
Ranked #79 on Image Classification on ImageNet
Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets.
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.