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Friday, June 21, 2019

Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings - implementation -

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We propose probabilistic models that can extrapolate learning curves of iterative machine learning algorithms, such as stochastic gradient descent for training deep networks, based on training data with variable-length learning curves. We study instantiations of this framework based on random forests and Bayesian recurrent neural networks. Our experiments show that these models yield better predictions than state-of-the-art models from the hyperparameter optimization literature when extrapolating the performance of neural networks trained with different hyperparameter settings.
An implementation is here: https://github.com/gmatilde/vdrnn

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