55 citations · 78 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
Kleanthis Malialis, Manuel Roveri, Cesare Alippi +2
In real-world applications, the process generating the data might suffer from nonstationary effects (e.g., due to seasonality, faults affecting sensors or actuators, and changes in…
Tiny Machine Learning for Concept Drift
Simone Disabato, Manuel Roveri
Tiny Machine Learning (TML) is a new research area whose goal is to design machine and deep learning techniques able to operate in Embedded Systems and IoT units, hence satisfying…
Time-Variant Variational Transfer for Value Functions
Giuseppe Canonaco, Andrea Soprani, Manuel Roveri +1
In most of the transfer learning approaches to reinforcement learning (RL) the distribution over the tasks is assumed to be stationary. Therefore, the target and source tasks are i…
A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service
Simone Disabato, Alessandro Falcetta, Alessio Mongelluzzo +1
Deep-learning-as-a-service is a novel and promising computing paradigm aiming at providing machine/deep learning solutions and mechanisms through Cloud-based computing infrastructu…
Distributed Deep Convolutional Neural Networks for the Internet-of-Things
Simone Disabato, Manuel Roveri, Cesare Alippi
Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically dema…