216 citations · 294 across the 13 of their papers we have counts for
15 papers
UNIFY: a Unified Policy Designing Framework for Solving Constrained Optimization Problems with Machine Learning
Mattia Silvestri, Allegra De Filippo, Michele Lombardi +1
The interplay between Machine Learning (ML) and Constrained Optimization (CO) has recently been the subject of increasing interest, leading to a new and prolific research area cove…
Deep Learning for Virus-Spreading Forecasting: a Brief Survey
Federico Baldo, Lorenzo Dall'Olio, Mattia Ceccarelli +5
The advent of the coronavirus pandemic has sparked the interest in predictive models capable of forecasting virus-spreading, especially for boosting and supporting decision-making…
An Analysis of Regularized Approaches for Constrained Machine Learning
Michele Lombardi, Federico Baldo, Andrea Borghesi +1
Regularization-based approaches for injecting constraints in Machine Learning (ML) were introduced to improve a predictive model via expert knowledge. We tackle the issue of findin…
Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey
Andrea Borghesi, Federico Baldo, Michela Milano
Deep Learning (DL) models proved themselves to perform extremely well on a wide variety of learning tasks, as they can learn useful patterns from large data sets. However, purely d…
Injecting Domain Knowledge in Neural Networks: a Controlled Experiment on a Constrained Problem
Mattia Silvestri, Michele Lombardi, Michela Milano
Given enough data, Deep Neural Networks (DNNs) are capable of learning complex input-output relations with high accuracy. In several domains, however, data is scarce or expensive t…
Combining Learning and Optimization for Transprecision Computing
Andrea Borghesi, Giuseppe Tagliavini, Michele Lombardi +2
The growing demands of the worldwide IT infrastructure stress the need for reduced power consumption, which is addressed in so-called transprecision computing by improving energy e…