4 citations · 5 across the 8 of their papers we have counts for
9 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…
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…
Continual Learning with Pretrained Backbones by Tuning in the Input Space
Simone Marullo, Matteo Tiezzi, Marco Gori +2
The intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks. This issue is critical in…
PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks
Enrico Meloni, Lapo Faggi, Simone Marullo +4
In this paper, we present PARTIME, a software library written in Python and based on PyTorch, designed specifically to speed up neural networks whenever data is continuously stream…
Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier
Simone Marullo, Matteo Tiezzi, Marco Gori +1
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…