96 citations · 216 across the 16 of their papers we have counts for
24 papers
On-Device Learning with Binary Neural Networks
Lorenzo Vorabbi, Davide Maltoni, Stefano Santi
Existing Continual Learning (CL) solutions only partially address the constraints on power, memory and computation of the deep learning models when deployed on low-power embedded C…
Arithmetic with Language Models: from Memorization to Computation
Davide Maltoni, Matteo Ferrara
A better understanding of the emergent computation and problem-solving capabilities of recent large language models is of paramount importance to further improve them and broaden t…
Detecting Morphing Attacks via Continual Incremental Training
Lorenzo Pellegrini, Guido Borghi, Annalisa Franco +1
Scenarios in which restrictions in data transfer and storage limit the possibility to compose a single dataset -- also exploiting different data sources -- to perform a batch-based…
Input Layer Binarization with Bit-Plane Encoding
Lorenzo Vorabbi, Davide Maltoni, Stefano Santi
Binary Neural Networks (BNNs) use 1-bit weights and activations to efficiently execute deep convolutional neural networks on edge devices. Nevertheless, the binarization of the fir…
On the challenges to learn from Natural Data Streams
Guido Borghi, Gabriele Graffieti, Davide Maltoni
In real-world contexts, sometimes data are available in form of Natural Data Streams, i.e. data characterized by a streaming nature, unbalanced distribution, data drift over a long…
Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Gabriele Graffieti +1
In recent years we have witnessed a renewed interest in machine learning methodologies, especially for deep representation learning, that could overcome basic i.i.d. assumptions an…