1 citations · 4 across the 11 of their papers we have counts for
13 papers
Least-time Gradient Flow
Alessandro Betti, Marco Gori, Stefano Melacci +1
Prescribing the speed of gradient flow on the risk itself, by the dynamics $\dot w=-u(E(w))\nabla E(w)/\abs{\nabla E(w)}^{2}$, makes the risk obey exa…
Book your room in the Turing Hotel! A symmetric and distributed Turing Test with multiple AIs and humans
Christian Di Maio, Tommaso Guidi, Luigi Quarantiello +4
In this paper, we report our experience with ``TuringHotel'', a novel extension of the Turing Test based on interactions within mixed communities of Large Language Models (LLMs) an…
Grounding Methods for Neural-Symbolic AI
Rodrigo Castellano Ontiveros, Francesco Giannini, Marco Gori +2
A large class of Neural-Symbolic (NeSy) methods employs a machine learner to process the input entities, while relying on a reasoner based on First-Order Logic to represent and pro…
Generative System Dynamics in Recurrent Neural Networks
Michele Casoni, Tommaso Guidi, Alessandro Betti +2
In this study, we investigate the continuous time dynamics of Recurrent Neural Networks (RNNs), focusing on systems with nonlinear activation functions. The objective of this work…
A Unified Framework for Neural Computation and Learning Over Time
Stefano Melacci, Alessandro Betti, Michele Casoni +3
This paper proposes Hamiltonian Learning, a novel unified framework for learning with neural networks "over time", i.e., from a possibly infinite stream of data, in an online manne…
Continual Learning of Conjugated Visual Representations through Higher-order Motion Flows
Simone Marullo, Matteo Tiezzi, Marco Gori +1
Learning with neural networks from a continuous stream of visual information presents several challenges due to the non-i.i.d. nature of the data. However, it also offers novel opp…
Dynamic Decoupling of Placid Terminal Attractor-based Gradient Descent Algorithm
Jinwei Zhao, Marco Gori, Alessandro Betti +4
Gradient descent (GD) and stochastic gradient descent (SGD) have been widely used in a large number of application domains. Therefore, understanding the dynamics of GD and improvin…
State-Space Modeling in Long Sequence Processing: A Survey on Recurrence in the Transformer Era
Matteo Tiezzi, Michele Casoni, Alessandro Betti +2
Effectively learning from sequential data is a longstanding goal of Artificial Intelligence, especially in the case of long sequences. From the dawn of Machine Learning, several re…
Explainable Malware Detection with Tailored Logic Explained Networks
Peter Anthony, Francesco Giannini, Michelangelo Diligenti +4
Malware detection is a constant challenge in cybersecurity due to the rapid development of new attack techniques. Traditional signature-based approaches struggle to keep pace with…
On the Resurgence of Recurrent Models for Long Sequences -- Survey and Research Opportunities in the Transformer Era
Matteo Tiezzi, Michele Casoni, Alessandro Betti +3
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. The outsta…
Nature-Inspired Local Propagation
Alessandro Betti, Marco Gori
The spectacular results achieved in machine learning, including the recent advances in generative AI, rely on large data collections. On the opposite, intelligent processes in natu…
Neural Time-Reversed Generalized Riccati Equation
Alessandro Betti, Michele Casoni, Marco Gori +3
Optimal control deals with optimization problems in which variables steer a dynamical system, and its outcome contributes to the objective function. Two classical approaches to sol…
Collectionless Artificial Intelligence
Marco Gori, Stefano Melacci
By and large, the professional handling of huge data collections is regarded as a fundamental ingredient of the progress of machine learning and of its spectacular results in relat…