The goal of this project is to reproduce the methods and experiments of the following paper:
C. Cortes, X. Gonzalvo, V. Kuznetsov, M. Mohri, S. Yang AdaNet: Adaptive Structural Learning of Artificial Neural Networks. We will try to reproduce their method that consists in building neural networks whose structure is learned and optimized at the same time as it’s weights.This method will be applied to a binary classification task on images from the CIFAR-10 dataset.

Mar 18, 2019
Luc Blassel & Romain Gautron

If you haven’t read my post on linear regression I invite you to do so here, but basically it is a method for modelling the relationship between variables \(X_i\) and a target feature \(y\) in a linear model. This modelling is done through learning weights \(\theta_i\) for each \(X_i\) supposing that our model looks something like this:

Luc Blassel