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Deep Active Learning: Enhancing Data Efficiency in Machine Learning - Lecture
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- 1 Introduction
- 2 About the Lab
- 3 Credit
- 4 Deep Learning
- 5 How hungry are these systems
- 6 More bang for the data
- 7 Label shift assumptions
- 8 Debt augmentation
- 9 Noise invariant representations
- 10 Transfer learning
- 11 Active Learning Approach
- 12 Denovo Active Learning
- 13 Active Learning Example
- 14 Active Learning Questions
- 15 Traditional Acquisition Functions
- 16 Dropout Regularization
- 17 Weight Uncertainty
- 18 Objective
- 19 Context
- 20 Thompson Sampling
- 21 Uncertainty Estimates
- 22 Data Hungry Tasks
- 23 Retraining
- 24 Problems
- 25 Active Learning with Partial Feedback
- 26 Expected Information Gain
- 27 Different Steps
- 28 Crowdsourcing
- 29 Labeling
- 30 The Worker
