Cloudera Fast Forward Labs’ latest research report and prototype explore learning with limited labeled data. This capability relaxes the stringent labeled data requirement in supervised machine learning and opens up new product possibilities. It is industry invariant, addresses the labeling pain point and enables applications to be built faster and more efficiently.

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LEARNING with LIMITED LABELED DATA
Shioulin Sam and Nisha Muktewar
Sanjay Krishnan (University of Chicago), Ines Montani (Explosion AI)

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Choosing what to label

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Random Sampling

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Uncertainty - Margin Sampling

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Uncertainty - Margin Sampling

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Extending to deep learning

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Distance from the decision boundary
Computing the distance between the datapoint and the closest neighbor of a different
class

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Distance from the decision boundary
Using adversarial perturbations to estimate the distance
Image from https://openai.com/blog/adversarial-example-research/

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Distance from the decision boundary
Computing the distance between the datapoint and the decision boundary using
perturbation magnitude

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Demo

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Ines Montani
Founder of Explosion AI

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Sanjay Krishnan
Assistant Professor of Computer Science,
University of Chicago

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Q & A

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THANK YOU

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