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Found 2 papers, 1 papers with code
A Study of the Learning Progress in Neural Architecture Search Techniques
As a surprising result, we find that the learning curves are completely flat, i. e., there is no observable progress of the controller in terms of the performance of its generated architectures.
On the Performance of Differential Evolution for Hyperparameter Tuning
This empirical study involves a range of different machine learning algorithms and datasets with various characteristics to compare the performance of Differential Evolution with Sequential Model-based Algorithm Configuration (SMAC), a reference Bayesian Optimization approach.
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