no code implementations • 12 Feb 2024 • Matteo Tiezzi, Michele Casoni, Alessandro Betti, Tommaso Guidi, Marco Gori, Stefano Melacci
A longstanding challenge for the Machine Learning community is the one of developing models that are capable of processing and learning from very long sequences of data.
no code implementations • 4 Feb 2024 • Alessandro Betti, Marco Gori
The spectacular results achieved in machine learning, including the recent advances in generative AI, rely on large data collections.
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.
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.
no code implementations • 30 Jun 2022 • Alessandro Betti, Marco Gori, Stefano Melacci
The remarkable progress in computer vision over the last few years is, by and large, attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired with the explosive growth of the GPU paradigm.
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 • 5 Apr 2022 • Alessandro Betti
Despite the breakthrough deep learning performances achieved for automatic object detection, small target detection is still a challenging problem, especially when looking at fast and accurate solutions suitable for mobile or edge applications.
no code implementations • 23 Nov 2021 • Antonio Di Tommaso, Alessandro Betti, Giacomo Fontanelli, Benedetto Michelozzi
As solar capacity installed worldwide continues to grow, there is an increasing awareness that advanced inspection systems are becoming of utmost importance to schedule smart interventions and minimize downtime likelihood.
1 code implementation • 15 Oct 2021 • Gabriele Ciravegna, Frédéric Precioso, Alessandro Betti, Kevin Mottin, Marco Gori
The deployment of Deep Learning (DL) models is still precluded in those contexts where the amount of supervised data is limited.
no code implementations • 12 Oct 2021 • Alessandro Betti, Marco Gori, Stefano Melacci, Marcello Pelillo, Fabio Roli
This paper sustains the position that the time has come for thinking of learning machines that conquer visual skills in a truly human-like context, where a few human-like object supervisions are given by vocal interactions and pointing aids only.
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.
1 code implementation • 28 Oct 2020 • Andrea Zugarini, Enrico Meloni, Alessandro Betti, Andrea Panizza, Marco Corneli, Marco Gori
We formulate the problem in terms of a functional risk that depends on the learning variables through the solutions of a dynamic system.
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.
no code implementations • 19 Jun 2020 • Lapo Faggi, Alessandro Betti, Dario Zanca, Stefano Melacci, Marco Gori
Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to most relevant locations in the visual field.
no code implementations • NeurIPS 2020 • Matteo Tiezzi, Stefano Melacci, Alessandro Betti, Marco Maggini, Marco Gori
In order to better structure the input probability distribution, we use a human-like focus of attention model that, coherently with the information maximization model, is also based on second-order differential equations.
no code implementations • 18 Feb 2020 • Giuseppe Marra, Matteo Tiezzi, Stefano Melacci, Alessandro Betti, Marco Maggini, Marco Gori
In this paper we study a constraint-based representation of neural network architectures.
no code implementations • 10 Feb 2020 • Alessandro Betti, Benedetto Michelozzi, Andrea Bracci, Andrea Masini
In this work a novel ships dataset is proposed consisting of more than 56k images of marine vessels collected by means of web-scraping and including 12 ship categories.
no code implementations • 10 Dec 2019 • Alessandro Betti, Marco Gori
The Backpropagation algorithm relies on the abstraction of using a neural model that gets rid of the notion of time, since the input is mapped instantaneously to the output.
no code implementations • 13 Nov 2019 • Alessandro Betti, Emanuele Crisostomi, Gianluca Paolinelli, Antonio Piazzi, Fabrizio Ruffini, Mauro Tucci
Hydropower plants are one of the most convenient option for power generation, as they generate energy exploiting a renewable source, they have relatively low operating and maintenance costs, and they may be used to provide ancillary services, exploiting the large reservoirs of available water.
no code implementations • 22 Oct 2019 • Lorenzo Gigoni, Alessandro Betti, Mauro Tucci, Emanuele Crisostomi
In this work, a novel predictive maintenance system is presented and applied to the main components of wind turbines.
no code implementations • 8 Oct 2019 • Michela Moschella, Mauro Tucci, Emanuele Crisostomi, Alessandro Betti
The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e. g., unit commitment, electricity market clearing or maintenance planning).
no code implementations • 1 Sep 2019 • Alessandro Betti, Marco Gori, Stefano Melacci
Humans are continuously exposed to a stream of visual data with a natural temporal structure.
no code implementations • 14 Jul 2019 • Alessandro Betti, Marco Gori
By and large the process of learning concepts that are embedded in time is regarded as quite a mature research topic.
no code implementations • 11 Jul 2019 • Alessandro Betti, Marco Gori
This paper proposes an in-depth re-thinking of neural computation that parallels apparently unrelated laws of physics, that are formulated in the variational framework of the least action principle.
no code implementations • 4 Jul 2019 • Alessandro Betti, Marco Gori
Machine Learning algorithms are typically regarded as appropriate optimization schemes for minimizing risk functions that are constructed on the training set, which conveys statistical flavor to the corresponding learning problem.
no code implementations • 26 Feb 2019 • Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri, Debora Mucci
The ability to accurately forecast power generation from renewable sources is nowadays recognised as a fundamental skill to improve the operation of power systems.
no code implementations • 29 Jan 2019 • Alessandro Betti, Maria Luisa Lo Trovato, Fabio Salvatore Leonardi, Giuseppe Leotta, Fabrizio Ruffini, Ciro Lanzetta
This paper presents a novel and flexible solution for fault prediction based on data collected from SCADA system.
no code implementations • 28 Aug 2018 • Alessandro Betti, Marco Gori, Stefano Melacci
A special choice of the functional index, which leads to forth-order differential equations---Cognitive Action Laws (CAL)---exhibits a structure that mirrors classic formulation of machine learning.
no code implementations • 21 Aug 2018 • Alessandro Betti, Marco Gori, Giuseppe Marra
This might open the doors to a truly novel class of learning algorithms where, because of the introduction of the notion of support neurons, the optimization scheme also plays a fundamental role in the construction of the architecture.
no code implementations • ICLR 2019 • Giuseppe Marra, Dario Zanca, Alessandro Betti, Marco Gori
The effectiveness of deep neural architectures has been widely supported in terms of both experimental and foundational principles.
no code implementations • 14 Jul 2018 • Giovanni Bellettini, Alessandro Betti, Marco Gori
By and large the behavior of stochastic gradient is regarded as a challenging problem, and it is often presented in the framework of statistical machine learning.
no code implementations • 14 Jul 2018 • Alessandro Betti, Marco Gori, Stefano Melacci
The puzzle of computer vision might find new challenging solutions when we realize that most successful methods are working at image level, which is remarkably more difficult than processing directly visual streams, just as happens in nature.
no code implementations • 16 Jan 2018 • Alessandro Betti, Marco Gori
Basically, while the theory enables the implementation of novel computer vision systems, it is also provides an intriguing explanation of the solution that evolution has discovered for humans, where it looks like that the video blurring in newborns and the day-night rhythm seem to emerge in a general computational framework, regardless of biology.