216 citations · 362 across the 14 of their papers we have counts for
7 papers · 1 filter
RUAD: unsupervised anomaly detection in HPC systems
Martin Molan, Andrea Borghesi, Daniele Cesarini +2
The increasing complexity of modern high-performance computing (HPC) systems necessitates the introduction of automated and data-driven methodologies to support system administrato…
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
Stefano Teso, Laurens Bliek, Andrea Borghesi +4
It is increasingly common to solve combinatorial optimisation problems that are partially-specified. We survey the case where the objective function or the relations between variab…
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…
Injective Domain Knowledge in Neural Networks for Transprecision Computing
Andrea Borghesi, Federico Baldo, Michele Lombardi +1
Machine Learning (ML) models are very effective in many learning tasks, due to the capability to extract meaningful information from large data sets. Nevertheless, there are learni…
Anomaly Detection using Autoencoders in High Performance Computing Systems
Andrea Borghesi, Andrea Bartolini, Michele Lombardi +2
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated an…