| Anomaly detection using autoencoders in high performance computing systems A Borghesi, A Bartolini, M Lombardi, M Milano, L Benini Proceedings of the AAAI Conference on artificial intelligence 33 (01), 9428-9433, 2019 | 278 | 2019 |
| Cost-based domain filtering F Focacci, A Lodi, M Milano Principles and Practice of Constraint Programming–CP’99: 5th International …, 1999 | 222 | 1999 |
| A hybrid exact algorithm for the TSPTW F Focacci, A Lodi, M Milano INFORMS journal on Computing 14 (4), 403-417, 2002 | 198 | 2002 |
| Global cut framework for removing symmetries F Focacci, M Milano Principles and Practice of Constraint Programming—CP 2001: 7th …, 2001 | 193 | 2001 |
| Communication-aware allocation and scheduling framework for stream-oriented multi-processor systems-on-chip M Ruggiero, A Guerri, D Bertozzi, F Poletti, M Milano Proceedings of the Design Automation & Test in Europe Conference 1, 6 pp., 2006 | 139 | 2006 |
| MAGMA: a multiagent architecture for metaheuristics M Milano, A Roli IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34 …, 2004 | 136 | 2004 |
| Constraint and integer programming M Milano, M Trick Constraint and integer programming: toward a unified methodology. Berlin …, 2004 | 124 | 2004 |
| Empirical decision model learning M Lombardi, M Milano, A Bartolini Artificial Intelligence 244, 343-367, 2017 | 122 | 2017 |
| A semisupervised autoencoder-based approach for anomaly detection in high performance computing systems A Borghesi, A Bartolini, M Lombardi, M Milano, L Benini Engineering Applications of Artificial Intelligence 85, 634-644, 2019 | 110 | 2019 |
| Optimal methods for resource allocation and scheduling: a cross-disciplinary survey M Lombardi, M Milano Constraints 17, 51-85, 2012 | 96 | 2012 |
| Learning techniques for automatic algorithm portfolio selection A Guerri, M Milano ECAI 16, 475, 2004 | 96 | 2004 |
| An efficient and complete approach for throughput-maximal SDF allocation and scheduling on multi-core platforms A Bonfietti, L Benini, M Lombardi, M Milano 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010 …, 2010 | 90 | 2010 |
| Allocation and scheduling for MPSoCs via decomposition and no-good generation L Benini, D Bertozzi, A Guerri, M Milano Principles and Practice of Constraint Programming-CP 2005: 11th …, 2005 | 83 | 2005 |
| Boosting combinatorial problem modeling with machine learning M Lombardi, M Milano arXiv preprint arXiv:1807.05517, 2018 | 81 | 2018 |
| Integrating constraint logic programming and operations research techniques for the crew rostering problem A Caprara, F Focacci, E Lamma, P Mello, M Milano, P Toth, D Vigo Software: Practice and Experience 28 (1), 49-76, 1998 | 81 | 1998 |
| Constraint propagation and value acquisition: why we should do it interactively E Lamma, P Mello, M Milano, R Cucchiara, M Gavanelli, M Piccardi IJCAI 99, 467-473, 1999 | 77 | 1999 |
| Solving tsp with time windows with constraints F Focacci, M Milano, A Lodi | 70 | 1999 |
| The need of multidisciplinary approaches and engineering tools for the development and implementation of the smart city paradigm O Andrisano, I Bartolini, P Bellavista, A Boeri, L Bononi, A Borghetti, ... Proceedings of the IEEE 106 (4), 738-760, 2018 | 65 | 2018 |
| Sustainable policy making: A strategic challenge for artificial intelligence M Milano, B O’Sullivan, M Gavanelli Ai Magazine 35 (3), 22-35, 2014 | 60 | 2014 |
| Predictive modeling for job power consumption in HPC systems A Borghesi, A Bartolini, M Lombardi, M Milano, L Benini High Performance Computing: 31st International Conference, ISC High …, 2016 | 59 | 2016 |
Willem-Jan van HoeveProfessor of Operations Research, Carnegie Mellon UniversityEmail confirmado em andrew.cmu.edu
