Jasper Snoek
Postdoctoral Fellow
Software
Spearmint (Bayesian Optimization)
Spearmint is a package to perform Bayesian optimization according to
the algorithms outlined in the paper:
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek, Hugo Larochelle and Ryan P. Adams
Advances in Neural Information Processing Systems, 2012
This code is designed to automatically run experiments (thus the code name 'spearmint') in a manner that iteratively adjusts a number of parameters so as to minimize some objective function in as few runs as possible.
The README file contains an explanation of the two packages and outlines how they can be used.
README FIRST
Spearmint
If you would like to contribute to spearmint, this package is also available as an open source project here.
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek, Hugo Larochelle and Ryan P. Adams
Advances in Neural Information Processing Systems, 2012
This code is designed to automatically run experiments (thus the code name 'spearmint') in a manner that iteratively adjusts a number of parameters so as to minimize some objective function in as few runs as possible.
The README file contains an explanation of the two packages and outlines how they can be used.
README FIRST
Spearmint
If you would like to contribute to spearmint, this package is also available as an open source project here.
Nonparametrically Guided Autoencoders
