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Hyperparameter Optimization

73 papers with code · Methodology
Subtask of AutoML

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Tune: A Research Platform for Distributed Model Selection and Training

13 Jul 2018ray-project/ray

We show that this interface meets the requirements for a broad range of hyperparameter search algorithms, allows straightforward scaling of search to large clusters, and simplifies algorithm implementation.

HYPERPARAMETER OPTIMIZATION MODEL SELECTION

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

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

Automating biomedical data science through tree-based pipeline optimization

28 Jan 2016rhiever/tpot

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.

HYPERPARAMETER OPTIMIZATION

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes

29 Aug 2017h2oai/h2o-3

The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared.

HYPERPARAMETER OPTIMIZATION

Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms

SCIPY 2013 2013 hyperopt/hyperopt

Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization.

HYPERPARAMETER OPTIMIZATION MODEL SELECTION

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.

HYPERPARAMETER OPTIMIZATION