4 citations · 5 across the 5 of their papers we have counts for
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BEP: A Binary Error Propagation Algorithm for Binary Neural Networks Training
Luca Colombo, Fabrizio Pittorino, Daniele Zambon +3
Binary Neural Networks (BNNs), which constrain both weights and activations to binary values, offer substantial reductions in computational complexity, memory footprint, and energy…
ReBoot: Encrypted Training of Deep Neural Networks with CKKS Bootstrapping
Alberto Pirillo, Luca Colombo
Growing concerns over data privacy underscore the need for deep learning methods capable of processing sensitive information without compromising confidentiality. Among privacy-enh…
Training Multi-Layer Binary Neural Networks With Local Binary Error Signals
Luca Colombo, Fabrizio Pittorino, Manuel Roveri
Binary Neural Networks (BNNs) significantly reduce computational complexity and memory usage in machine and deep learning by representing weights and activations with just one bit.…
TIFeD: a Tiny Integer-based Federated learning algorithm with Direct feedback alignment
Luca Colombo, Alessandro Falcetta, Manuel Roveri
Training machine and deep learning models directly on extremely resource-constrained devices is the next challenge in the field of tiny machine learning. The related literature in…
NITRO-D: Native Integer-only Training of Deep Convolutional Neural Networks
Alberto Pirillo, Luca Colombo, Manuel Roveri
Quantization is a pivotal technique for managing the growing computational and memory demands of Deep Neural Networks (DNNs). By reducing the number of bits used to represent weigh…