316 citations · 348 across the 15 of their papers we have counts for
8 papers · 1 filter
PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks
Enrico Meloni, Lapo Faggi, Simone Marullo +4
In this paper, we present PARTIME, a software library written in Python and based on PyTorch, designed specifically to speed up neural networks whenever data is continuously stream…
Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier
Simone Marullo, Matteo Tiezzi, Marco Gori +1
In the last decade, motivated by the success of Deep Learning, the scientific community proposed several approaches to make the learning procedure of Neural Networks more effective…
Developing Constrained Neural Units Over Time
Alessandro Betti, Marco Gori, Simone Marullo +1
In this paper we present a foundational study on a constrained method that defines learning problems with Neural Networks in the context of the principle of least cognitive action,…
Focus of Attention Improves Information Transfer in Visual Features
Matteo Tiezzi, Stefano Melacci, Alessandro Betti +2
Unsupervised learning from continuous visual streams is a challenging problem that cannot be naturally and efficiently managed in the classic batch-mode setting of computation. The…
Local Propagation in Constraint-based Neural Network
Giuseppe Marra, Matteo Tiezzi, Stefano Melacci +3
In this paper we study a constraint-based representation of neural network architectures. We cast the learning problem in the Lagrangian framework and we investigate a simple optim…
A Lagrangian Approach to Information Propagation in Graph Neural Networks
Matteo Tiezzi, Giuseppe Marra, Stefano Melacci +2
In many real world applications, data are characterized by a complex structure, that can be naturally encoded as a graph. In the last years, the popularity of deep learning techniq…