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Characterizing the hyper-parameter space of LSTM language models for
mixed context applications
release_oz3pmfvvk5eixaggbs2ynojcgy
by
Victor Akinwande, Sekou L. Remy
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as a article
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2017
Abstract
Applying state of the art deep learning models to novel real world datasets
gives a practical evaluation of the generalizability of these models. Of
importance in this process is how sensitive the hyper parameters of such models
are to novel datasets as this would affect the reproducibility of a model. We
present work to characterize the hyper parameter space of an LSTM for language
modeling on a code-mixed corpus. We observe that the evaluated model shows
minimal sensitivity to our novel dataset bar a few hyper parameters.
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1712.03199v1
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