activity
20182021
most citedConvolutional Networks in Visual Environments

5 citations · 10 across the 6 of their papers we have counts for

collaborators

18 papers

cs.CV2022

Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams

Matteo Tiezzi, Simone Marullo, Lapo Faggi +3

Devising intelligent agents able to live in an environment and learn by observing the surroundings is a longstanding goal of Artificial Intelligence. From a bare Machine Learning p…

cs.CV2021

Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments

Enrico Meloni, Alessandro Betti, Lapo Faggi +3

Continual learning refers to the ability of humans and animals to incrementally learn over time in a given environment. Trying to simulate this learning process in machines is a ch…

cs.LG2020

An Optimal Control Approach to Learning in SIDARTHE Epidemic model

Andrea Zugarini, Enrico Meloni, Alessandro Betti +3

The COVID-19 outbreak has stimulated the interest in the proposal of novel epidemiological models to predict the course of the epidemic so as to help planning effective control str…

cs.LG2020

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,…

cs.LG20204 cited

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…

cs.LG2020

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…