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Practical Application: Rank Minimization in NN Layers
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Classroom Contents
Generalization in Deep Learning Through the Lens of Implicit Rank Minimization
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- 1 Intro
- 2 Generalization via Bis-Variance Tradeoff
- 3 Generalization in Deep Learning
- 4 Linear Models: Implicit Norm Minimization Linear Regression
- 5 Implicit Norm Minimization In Deep Learning?
- 6 Perspective: Implicit Rank Minimization
- 7 Outline
- 8 Matrix Completion Two-Dimensional Prediction
- 9 MF Linear NN
- 10 Conjecture: Implicit Nuclear Norm Minimization
- 11 Dynamical Analysis of Implicit Regularization in MF
- 12 Implicit Regularization in MF Norm Minimization Does the implicit regularization in MF minimize a norm?
- 13 Drawbacks of Studying MF
- 14 Tensor Completion Multi-Dimensional Prediction
- 15 TF Shallow Non-Linear Convolutional NN
- 16 Dynamical Analysis of Implicit Regularization in TF
- 17 Analogy Between Implicit Regularizations
- 18 HTF Deep Non-Linear CNN TF does not account for depth
- 19 Dynamical Analysis of Implicit Regularization in HTF
- 20 Practical Application: Rank Minimization in NN Layers
- 21 Potential Explanation for Generalization on Natural Data
- 22 Countering Locality of CNNs via Regularization
- 23 Recap
- 24 Implicit Rank Minimization in Deep Learning
