Search Results
Is deep learning necessary for simple classification tasks?
providing a head-to-head comparison of AutoML and DL in the context of binary classification on 6 well-characterized public datasets, and (2.)
Benchmarking Automatic Machine Learning Frameworks
AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process.
Automating biomedical data science through tree-based pipeline optimization
Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government.
Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool
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.
Layered TPOT: Speeding up Tree-based Pipeline Optimization
With the demand for machine learning increasing, so does the demand for tools which make it easier to use.
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
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.
Multilevel Delayed Acceptance MCMC
We develop a novel Markov chain Monte Carlo (MCMC) method that exploits a hierarchy of models of increasing complexity to efficiently generate samples from an unnormalized target distribution.
Methodology Computation 62F15, 62M05, 65C05, 65C40
FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs
R is one of the most popular programming languages for statistics and machine learning, but the R framework is relatively slow and unable to scale to large datasets.
Distributed, Parallel, and Cluster Computing
Launchpad: A Programming Model for Distributed Machine Learning Research
A major driver behind the success of modern machine learning algorithms has been their ability to process ever-larger amounts of data.
Distributed, Parallel, and Cluster Computing
A Library for Representing Python Programs as Graphs for Machine Learning
Graph representations of programs are commonly a central element of machine learning for code research.
