1 citations · 2 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams
Matteo Tiezzi, Simone Marullo, Lapo Faggi +3
Devising intelligent agents able to live in an environment and learn by observing the surroundings is a longstanding goal of Artificial Intelligence. From a bare Machine Learning p…
Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks
Simone Marullo, Matteo Tiezzi, Marco Gori +1
Amongst a variety of approaches aimed at making the learning procedure of neural networks more effective, the scientific community developed strategies to order the examples accord…