Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network models, layers composing the neural models are simultaneously trained using backpropagation. We argue that the isolated t...
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Main Authors | , , , , , , |
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Format | Journal Article |
Language | English |
Published |
10.06.2019
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Subjects | |
Online Access | Get full text |
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Summary: | Classical Machine Learning (ML) pipelines often comprise of multiple ML
models where models, within a pipeline, are trained in isolation. Conversely,
when training neural network models, layers composing the neural models are
simultaneously trained using backpropagation. We argue that the isolated
training scheme of ML pipelines is sub-optimal, since it cannot jointly
optimize multiple components. To this end, we propose a framework that
translates a pre-trained ML pipeline into a neural network and fine-tunes the
ML models within the pipeline jointly using backpropagation. Our experiments
show that fine-tuning of the translated pipelines is a promising technique able
to increase the final accuracy. |
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DOI: | 10.48550/arxiv.1906.03822 |