6 citations · 35 across the 25 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…