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The Continuum Idealization of Residual Networks
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Classroom Contents
Approximation Theory of Deep Learning from the Dynamical Viewpoint
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- 1 Intro
- 2 Deep Learning: Theory vs Practice
- 3 Composition is Dynamics
- 4 Supervised Learning
- 5 The Problem of Approximation
- 6 Example: Approximation by Trigonometric Polynomials
- 7 The Continuum Idealization of Residual Networks
- 8 How do dynamics approximate functions?
- 9 Universal Approximation by Dynamics
- 10 Approximation of Symmetric Functions by Dynamical Hypothesis Spac
- 11 Sequence Modelling Applications
- 12 DL Architectures for Sequence Modelling
- 13 Modelling Static vs Dynamic Relationships
- 14 An Approximation Theory for Sequence Modelling
- 15 The Recurrent Neural Network Hypothesis Space
- 16 The Linear RNN Hypothesis Space
- 17 Properties of Linear RNN Hypothesis Space
- 18 Approximation Guarantee (Density)
- 19 Smoothness and Memory
- 20 Insights on the (Linear) RNN Hypothesis Space
- 21 Convolutional Architectures
- 22 Encoder-Decoder Architectures
- 23 Extending the RNN Analysis
