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Optimal Siting and Sizing of Wayside Energy Storage Systems in a D.C. Railway Line

Energies (Basel), 2020-12, Vol.13 (23), p.6271

2020

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Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis

PloS one, 2021-12, Vol.16 (12), p.e0261511-e0261511

2021

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Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

PloS one, 2020-03, Vol.15 (3), p.e0230219-e0230219

2020

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Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

Life (Basel, Switzerland), 2021-02, Vol.11 (2), p.122

2021

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Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree

EURO journal on computational optimization, 2024, Vol.12, p.100084, Article 100084

2024

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Global optimization issues in deep network regression: an overview

Journal of global optimization, 2019-02, Vol.73 (2), p.239-277

Palagi, Laura

2019

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Optimal Network Design for Municipal Waste Management: Application to the Metropolitan City of Rome

Logistics, 2024-09, Vol.8 (3), p.79

2024

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Data of patients undergoing rehabilitation programs

Data in brief, 2020-06, Vol.30, p.105419-105419, Article 105419

2020

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Deep Neural Network Regression to Assist Non-Invasive Diagnosis of Portal Hypertension

Healthcare (Basel), 2023-09, Vol.11 (18), p.2603

2023

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On the convergence of a Block-Coordinate Incremental Gradient method

Soft computing (Berlin, Germany), 2021-10, Vol.25 (19), p.12615-12626

2021

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A neural network approach to the combined multi-objective optimization of the thermodynamic cycle and the radial inflow turbine for Organic Rankine cycle applications

Applied energy, 2019-03, Vol.237, p.210-226

2019

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SpeeDP: an algorithm to compute SDP bounds for very large Max-Cut instances

Mathematical programming, 2012-12, Vol.136 (2), p.353-373

2012

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A MILP methodology to optimize sizing of PV - Wind renewable energy systems

Energy (Oxford), 2018-12, Vol.165, p.385-398

2018

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Branching with hyperplanes in the criterion space: The frontier partitioner algorithm for biobjective integer programming

European journal of operational research, 2020-05, Vol.283 (1), p.57-69

2020

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IN TRIDUO DIE FESTA (PETR. SAT. 45.4): IPOTESI SULLA DATA DELLA CENA DI TRIMALCHIONE

Prometheus (Firenze), 2023-01, Vol.49, p.157-167

Palagi, Laura Bocciolini

2023

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Machine Learning for the prediction of the dynamic behavior of a small scale ORC system

Energy (Oxford), 2019-01, Vol.166, p.72-82

2019

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Margin optimal classification trees

Computers & operations research, 2024-01, Vol.161, p.106441, Article 106441

2024

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A compact formulation for the base station deployment problem in wireless networks

Networks, 2023-07, Vol.82 (1), p.52-67

2023

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LE 'PRETESE' DI UN MAESTRO (NOTA A PETRONIO, SAT. 46.5)

Prometheus (Firenze), 2022-01, Vol.48, p.164-178

Palagi, Laura Bocciolini

2022

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Using SVM to combine global heuristics for the Standard Quadratic Problem

European journal of operational research, 2015-03, Vol.241 (3), p.596-605

2015

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Block layer decomposition schemes for training deep neural networks

Journal of global optimization, 2020-05, Vol.77 (1), p.97-124

2020

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A truncated Newton method in an augmented Lagrangian framework for nonlinear programming

Computational optimization and applications, 2010-03, Vol.45 (2), p.311-352

2010

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Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training

Computational optimization and applications, 2018-09, Vol.71 (1), p.115-145

2018

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A distributed bandwidth allocation algorithm for bluetooth wireless networks based on Wardrop equilibrium

2015 34th Chinese Control Conference (CCC), 2015, p.6469-6474

2015

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Tuning parameters of deep neural network training algorithms pays off: a computational study

TOP, 2024-10, Vol.32 (3), p.579-620

2024

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Optimal Siting and Sizing of Wayside Energy Storage Systems in a D.C. Railway Line

Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree

Global optimization issues in deep network regression: an overview

Optimal Network Design for Municipal Waste Management: Application to the Metropolitan City of Rome

Data of patients undergoing rehabilitation programs

Deep Neural Network Regression to Assist Non-Invasive Diagnosis of Portal Hypertension

On the convergence of a Block-Coordinate Incremental Gradient method

A neural network approach to the combined multi-objective optimization of the thermodynamic cycle and the radial inflow turbine for Organic Rankine cycle applications

SpeeDP: an algorithm to compute SDP bounds for very large Max-Cut instances

A MILP methodology to optimize sizing of PV - Wind renewable energy systems

Branching with hyperplanes in the criterion space: The frontier partitioner algorithm for biobjective integer programming

IN TRIDUO DIE FESTA (PETR. SAT. 45.4): IPOTESI SULLA DATA DELLA CENA DI TRIMALCHIONE

Machine Learning for the prediction of the dynamic behavior of a small scale ORC system

Margin optimal classification trees

A compact formulation for the base station deployment problem in wireless networks

LE 'PRETESE' DI UN MAESTRO (NOTA A PETRONIO, SAT. 46.5)

Using SVM to combine global heuristics for the Standard Quadratic Problem

Block layer decomposition schemes for training deep neural networks

A truncated Newton method in an augmented Lagrangian framework for nonlinear programming

Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training

A distributed bandwidth allocation algorithm for bluetooth wireless networks based on Wardrop equilibrium

Tuning parameters of deep neural network training algorithms pays off: a computational study

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