no code implementations • 28 Dec 2022 • Mattia G. Bergomi, Massimo Ferri, Alessandro Mella, Pietro Vertechi
Artificial neural networks can learn complex, salient data features to achieve a given task.
no code implementations • 1 Aug 2022 • Mattia G. Bergomi, Pietro Vertechi
Properties such as composability and automatic differentiation made artificial neural networks a pervasive tool in applications.
no code implementations • 27 Apr 2022 • Pietro Vertechi, Mattia G. Bergomi
We provide a unifying framework where artificial neural networks and their architectures can be formally described as particular cases of a general mathematical construction--machines of finite depth.
2 code implementations • 6 Jul 2020 • Pietro Vertechi, Mattia G. Bergomi
Using tools from topology and functional analysis, we provide a framework where artificial neural networks, and their architectures, can be formally described.
1 code implementation • 19 Jan 2019 • Mattia G. Bergomi, Massimo Ferri, Pietro Vertechi, Lorenzo Zuffi
Nowadays, data generation, representation and analysis occupy central roles in human society.
Combinatorics Category Theory
no code implementations • NeurIPS 2014 • Pietro Vertechi, Wieland Brendel, Christian K. Machens
Specifically, we show how these networks can learn to efficiently represent their present and past inputs, based on local learning rules only.