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Hyperparameter Optimization: A Spectral Approach
[article]
2018
arXiv
pre-print
We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters. The algorithm --- an iterative application of compressed sensing techniques for orthogonal polynomials --- requires only uniform sampling of the hyperparameters and is thus easily parallelizable. Experiments for training deep neural
arXiv:1706.00764v4
fatcat:g5lljknufzcf3bwsnqasukfoay