no code implementations • 17 Apr 2024 • Emanuele La Malfa, Gabriele La Malfa, Giuseppe Nicosia, Vito Latora
For the novel metrics, in addition to the existing ones, we provide a mathematical formalisation for Fully Connected, AutoEncoder, Convolutional and Recurrent neural networks, of which we vary the activation functions and the number of hidden layers.
no code implementations • 12 Sep 2022 • Emanuele La Malfa, Gabriele La Malfa, Claudio Caprioli, Giuseppe Nicosia, Vito Latora
Deep Neural Networks are, from a physical perspective, graphs whose `links` and `vertices` iteratively process data and solve tasks sub-optimally.
1 code implementation • 6 Oct 2021 • Emanuele La Malfa, Gabriele La Malfa, Giuseppe Nicosia, Vito Latora
In this paper, we interpret Deep Neural Networks with Complex Network Theory.
