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Classroom Contents
Understanding Deep Neural Networks - From Generalization to Interpretability
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- 1 Intro
- 2 The Dawn of Deep Learning
- 3 Impact of Deep Learning on Mathematical Problems
- 4 Numerical Results
- 5 Graph Convolutional Neural Networks Graph convolutional neural networks
- 6 Two Approaches to Convolution on Graphs
- 7 Spectral Graph Convolution
- 8 Spectral Filtering using Functional Calculus
- 9 Graphs Modeling the Same Phenomenon
- 10 Comparing the Repercussion of a Filter on Two Graphs
- 11 Transferability of Functional Calculus Filters
- 12 Rethinking Transferability
- 13 Fundamental Questions concerning Deep Neural Networks
- 14 General Problem Setting
- 15 What is Relevance?
- 16 The Relevance Mapping Problem
- 17 Rate-Distortion Viewpoint
- 18 Problem Relaxation
- 19 Observations
- 20 MNIST Experiment
