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Machine Learning with Limited Labeled Data 4/3/19
The document discusses methodologies for selecting labeled data in machine learning, particularly under conditions of limited labeled data. It introduces concepts such as random sampling and uncertainty margin sampling, along with techniques for estimating distances from decision boundaries using adversarial perturbations. The document also acknowledges contributions from experts affiliated with the University of Chicago and Explosion AI.
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Machine Learning with Limited Labeled Data 4/3/19
- 1. Confidential – Restricted LEARNINGwith LIMITED LABELED DATA Shioulin Sam and Nisha Muktewar Sanjay Krishnan (University of Chicago), Ines Montani (Explosion AI)
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- 17. Confidential – Restricted Choosingwhat to label
- 18. Confidential – Restricted18 Random Sampling
- 19. Confidential – Restricted19 Uncertainty - Margin Sampling
- 20. Confidential – Restricted20 Uncertainty - Margin Sampling
- 21. Confidential – Restricted Extendingto deep learning
- 22. Confidential – Restricted22 Distance from the decision boundary Computing the distance between the datapoint and the closest neighbor of a different class
- 23. Confidential – Restricted23 Distance from the decision boundary Using adversarial perturbations to estimate the distance Image from https://openai.com/blog/adversarial-example-research/
- 24. Confidential – Restricted24 Distance from the decision boundary Computing the distance between the datapoint and the decision boundary using perturbation magnitude
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- 30. Confidential – Restricted30 Ines Montani Founder of Explosion AI
- 31. Confidential – Restricted31 Sanjay Krishnan Assistant Professor of Computer Science, University of Chicago
- 32. Confidential – Restricted32 Q & A
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