  In recent years, Deep Learning methods have achieved state of the art
performance in a vast range of machine learning tasks, including image
classification and multilingual automatic text translation. These architectures
are trained to solve machine learning tasks in an end-to-end fashion. In order
to reach top-tier performance, these architectures often require a very large
number of trainable parameters. There are multiple undesirable consequences,
and in order to tackle these issues, it is desired to be able to open the black
boxes of deep learning architectures. Problematically, doing so is difficult
due to the high dimensionality of representations and the stochasticity of the
training process. In this thesis, we investigate these architectures by
introducing a graph formalism based on the recent advances in Graph Signal
Processing (GSP). Namely, we use graphs to represent the latent spaces of deep
neural networks. We showcase that this graph formalism allows us to answer
various questions including: ensuring generalization abilities, reducing the
amount of arbitrary choices in the design of the learning process, improving
robustness to small perturbations added to the inputs, and reducing
computational complexity
