
Deep Learning for VirusSpreading Forecasting: a Brief Survey
The advent of the coronavirus pandemic has sparked the interest in predi...
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Discrete solution pools and noisecontrastive estimation for predictandoptimize
Numerous reallife decisionmaking processes involve solving a combinato...
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An Analysis of Regularized Approaches for Constrained Machine Learning
Regularizationbased approaches for injecting constraints in Machine Lea...
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Teaching the Old Dog New Tricks: Supervised Learning with Constraints
Methods for taking into account external knowledge in Machine Learning m...
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Injecting Domain Knowledge in Neural Networks: a Controlled Experiment on a Constrained Problem
Given enough data, Deep Neural Networks (DNNs) are capable of learning c...
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Injective Domain Knowledge in Neural Networks for Transprecision Computing
Machine Learning (ML) models are very effective in many learning tasks, ...
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Combining Learning and Optimization for Transprecision Computing
The growing demands of the worldwide IT infrastructure stress the need f...
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A Lagrangian Dual Framework for Deep Neural Networks with Constraints
A variety of computationally challenging constrained optimization proble...
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Anomaly Detection using Autoencoders in High Performance Computing Systems
Anomaly detection in supercomputers is a very difficult problem due to t...
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Boosting Combinatorial Problem Modeling with Machine Learning
In the past few years, the area of Machine Learning (ML) has witnessed t...
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On Trade in Bilateral Oligopolies with Altruistic and Spiteful Agents
This paper studies the effects of altruism and spitefulness in a twosid...
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A Visual Web Tool to Perform WhatIf Analysis of Optimization Approaches
In Operation Research, practical evaluation is essential to validate the...
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Michele Lombardi
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