Reading group: Underpinning techniques of most widely used DNN architectures

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• Nicolai Baldin (Statslab)
• Tuesday 31 October 2017, 14:00-15:00
• Centre for Mathematical Sciences, MR2.

If you have a question about this talk, please contact Nicolai Baldin.

In the first session we will look more closely into common techniques of widely used NN architectures like batch normalisation, dropout and stochastic optimisers. We shall also touch upon regularisation ideas and various activation functions.

It will be roughly based upon the following papers:

1. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift pdf

2. Dropout: A Simple Way to Prevent Neural Networks from Overfitting pdf

3. Adam: A Method for Stochastic Optimization pdf

The first session will be given by the organisers but participants are expected to be familiar with the papers. More information about the reading group can be found at mathsml.com

This talk is part of the Mathematics and Machine Learning series.

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