316 citations · 347 across the 7 of their papers we have counts for
15 papers
Gravitational Models Explain Shifts on Human Visual Attention
Dario Zanca, Marco Gori, Stefano Melacci +1
Visual attention refers to the human brain's ability to select relevant sensory information for preferential processing, improving performance in visual and cognitive tasks. It pro…
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…
Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach
Dario Zanca, Stefano Melacci, Marco Gori
Human visual attention is a complex phenomenon. A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient loca…