Hyperparameter Optimization Tool using Surrogate Modeling and Uncertainty Quantification.
While there is already a plethora of hyperparameter optimization tools publicly available for machine learning applications, none of them take into account an essential aspect of such models which is that neural network models are built using a stochastic solver approach. The HPO-UQ software was developed with the goal of implementing uncertainty quantification on the models’ parameters.
HYPPO Copyright (c) 2021, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.
If you have questions about your rights to use or distribute this software, please contact Berkeley Lab’s Intellectual Property Office at IPO@lbl.gov.
NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.
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