HMCNAS: Neural Architecture Search using Hidden Markov Chains and Bayesian Optimization

31 Jul 2020Vasco LopesLuís A. Alexandre

Neural Architecture Search has achieved state-of-the-art performance in a variety of tasks, out-performing human-designed networks. However, many assumptions, that require human definition, related with the problems being solved or the models generated are still needed: final model architectures, number of layers to be sampled, forced operations, small search spaces, which ultimately contributes to having models with higher performances at the cost of inducing bias into the system... (read more)

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