Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach

CoRR, 2019.

Cited by: 3|Views85
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Abstract:

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-...More

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