We use this repository to keep track of slides that we are making for a theoretical review on neural network based models.

The following is a list of papers that we are working on presentatoin slides.

• The PDF files of the corresponding papers are in folder "papers".
• The corresponding Latex sources are in folder "slides source files".

1. Nonparametric regression using deep neural networks with ReLU activation function; J Schmidt-Hieber - arXiv preprint arXiv:1708.06633, 2017

• papers/1708.06633.pdf
• slides source files/Hieber_approx.xxx for the functional approximation part
• slides source files/Hieber_Risk.xxx for the minimax estimation rate part

2. Optimal approximation of piecewise smooth functions using deep ReLU neural networks; P Petersen, F Voigtlaender - Neural Networks, 2018 - Elsevier

• papers/1709.05289.pdf
• slides source files/Petersen.xxx

3. Error bounds for approximations with deep ReLU networks; D Yarotsky - Neural Networks, 2017 - Elsevier

• papers/1610.01145.pdf
• slides source files/Yarotsky.xxx

The following papers are possibly in the pipeline.

4. Universality of deep convolutional neural networks; DX Zhou - Applied and computational harmonic analysis, 2020 - Elsevier

• papers/1805.10769.pdf

5. Fast learning rates for plug-in classifiers; JY Audibert, AB Tsybakov - The Annals of statistics, 2007

• papers/1183667286.pdf

6. Optimal aggregation of classifiers in statistical learning; AB Tsybakov - The Annals of Statistics, 2004

• papers/1079120131.pdf

7. Smooth discrimination analysis; E Mammen, AB Tsybakov - The Annals of Statistics

• papers/1017939240.pdf