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Machine Learning with Limited Labeled Data 4/3/19

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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.

Publié dans : Technologie
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Machine Learning with Limited Labeled Data 4/3/19

  1. 1. Confidential – Restricted LEARNING with LIMITED LABELED DATA Shioulin Sam and Nisha Muktewar Sanjay Krishnan (University of Chicago), Ines Montani (Explosion AI)
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  16. 16. Confidential – Restricted Choosing what to label
  17. 17. Confidential – Restricted 18 Random Sampling
  18. 18. Confidential – Restricted 19 Uncertainty - Margin Sampling
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  20. 20. Confidential – Restricted Extending to deep learning
  21. 21. Confidential – Restricted 22 Distance from the decision boundary Computing the distance between the datapoint and the closest neighbor of a different class
  22. 22. Confidential – Restricted 23 Distance from the decision boundary Using adversarial perturbations to estimate the distance Image from https://openai.com/blog/adversarial-example-research/
  23. 23. Confidential – Restricted 24 Distance from the decision boundary Computing the distance between the datapoint and the decision boundary using perturbation magnitude
  24. 24. Confidential – Restricted Demo
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  29. 29. Confidential – Restricted 30 Ines Montani Founder of Explosion AI
  30. 30. Confidential – Restricted 31 Sanjay Krishnan Assistant Professor of Computer Science, University of Chicago
  31. 31. Confidential – Restricted 32 Q & A
  32. 32. Confidential – Restricted THANK YOU
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