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Meta-Learning - Why It’s Hard and What We Can Do - Ke Li
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- 1 Introduction
- 2 HighParameter Optimization
- 3 Automatic Model Selection
- 4 Program Induction
- 5 Design Considerations
- 6 Proof
- 7 Framework
- 8 Optimization Based Mental Learning
- 9 Objective Functions
- 10 Preventing Overfitting
- 11 Forward Dynamics
- 12 Uncertainty
- 13 Forward Dynamic Stochastic
- 14 Original Formulation
- 15 New Formulation
- 16 Expectation
- 17 Setting
- 18 Example
- 19 Quick Question
- 20 Update Formula
- 21 NeuralNets
- 22 Experiments
- 23 Parameters
- 24 Intervals
- 25 Gradients
- 26 Illustration
- 27 Outputs
- 28 empirical learning
- 29 correct yourself
- 30 speed up
