no code implementations • 14 Dec 2023 • Alessandro Betti, Michele Casoni, Marco Gori, Simone Marullo, Stefano Melacci, Matteo Tiezzi
This paper introduces a novel neural-based approach to optimal control, with the aim of working forward-in-time.
no code implementations • 5 Jun 2023 • Simone Marullo, Matteo Tiezzi, Marco Gori, Stefano Melacci, Tinne Tuytelaars
The intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks.
1 code implementation • 17 Oct 2022 • Enrico Meloni, Lapo Faggi, Simone Marullo, Alessandro Betti, Matteo Tiezzi, Marco Gori, Stefano Melacci
nature of the streamed data with samples that are smoothly evolving over time for efficient gradient computations.
1 code implementation • 26 Apr 2022 • Matteo Tiezzi, Simone Marullo, Lapo Faggi, Enrico Meloni, Alessandro Betti, Stefano Melacci
Our experiments leverage 3D virtual environments and they show that the proposed agents can learn to distinguish objects just by observing the video stream.
no code implementations • 16 Sep 2021 • Enrico Meloni, Alessandro Betti, Lapo Faggi, Simone Marullo, Matteo Tiezzi, Stefano Melacci
However, in order to devise continual learning algorithms that operate in more realistic conditions, it is fundamental to gain access to rich, fully customizable and controlled experimental playgrounds.
no code implementations • 21 Jun 2021 • Simone Marullo, Matteo Tiezzi, Marco Gori, Stefano Melacci
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.
no code implementations • 1 Sep 2020 • Alessandro Betti, Marco Gori, Simone Marullo, Stefano Melacci
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, which very much resembles the principle of least action in mechanics.