| 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 | 264 | 2019 |
| Empirical decision model learning M Lombardi, M Milano, A Bartolini Artificial Intelligence 244, 343-367, 2017 | 119 | 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 | 107 | 2019 |
| Optimal methods for resource allocation and scheduling: a cross-disciplinary survey M Lombardi, M Milano Constraints 17, 51-85, 2012 | 96 | 2012 |
| 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 |
| Lagrangian duality for constrained deep learning F Fioretto, P Van Hentenryck, TWK Mak, C Tran, F Baldo, M Lombardi Machine Learning and Knowledge Discovery in Databases. Applied Data Science …, 2021 | 81 | 2021 |
| Boosting combinatorial problem modeling with machine learning M Lombardi, M Milano arXiv preprint arXiv:1807.05517, 2018 | 77 | 2018 |
| 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 | 58 | 2016 |
| Robust scheduling of task graphs under execution time uncertainty M Lombardi, M Milano, L Benini IEEE transactions on computers 62 (1), 98-111, 2011 | 51 | 2011 |
| Contrastive losses and solution caching for predict-and-optimize M Mulamba, J Mandi, M Diligenti, M Lombardi, V Bucarey, T Guns arXiv preprint arXiv:2011.05354, 2020 | 48 | 2020 |
| Embedding decision trees and random forests in constraint programming A Bonfietti, M Lombardi, M Milano Integration of AI and OR Techniques in Constraint Programming: 12th …, 2015 | 48 | 2015 |
| Throughput constraint for synchronous data flow graphs A Bonfietti, M Lombardi, M Milano, L Benini Integration of AI and OR Techniques in Constraint Programming for …, 2009 | 44 | 2009 |
| Stochastic allocation and scheduling for conditional task graphs in multi-processor systems-on-chip M Lombardi, M Milano, M Ruggiero, L Benini Journal of scheduling 13, 315-345, 2010 | 41 | 2010 |
| Neuron constraints to model complex real-world problems A Bartolini, M Lombardi, M Milano, L Benini Principles and Practice of Constraint Programming–CP 2011: 17th …, 2011 | 39 | 2011 |
| A constraint programming scheduler for heterogeneous high-performance computing machines T Bridi, A Bartolini, M Lombardi, M Milano, L Benini IEEE transactions on parallel and distributed systems 27 (10), 2781-2794, 2016 | 35 | 2016 |
| Scheduling-based power capping in high performance computing systems A Borghesi, A Bartolini, M Lombardi, M Milano, L Benini Sustainable Computing: Informatics and Systems 19, 1-13, 2018 | 33 | 2018 |
| Power capping in high performance computing systems A Borghesi, F Collina, M Lombardi, M Milano, L Benini Principles and Practice of Constraint Programming: 21st International …, 2015 | 32 | 2015 |
| Ms3: A mediterranean-stile job scheduler for supercomputers-do less when it's too hot! A Borghesi, C Conficoni, M Lombardi, A Bartolini 2015 International Conference on High Performance Computing & Simulation …, 2015 | 31 | 2015 |
| Injecting domain knowledge in neural networks: a controlled experiment on a constrained problem M Silvestri, M Lombardi, M Milano Integration of Constraint Programming, Artificial Intelligence, and …, 2021 | 29 | 2021 |
| Teaching the old dog new tricks: Supervised learning with constraints F Detassis, M Lombardi, M Milano Proceedings of the AAAI Conference on Artificial Intelligence 35 (5), 3742-3749, 2021 | 28 | 2021 |
Michela MilanoProfessor of Intelligent SystemsVerified email at unibo.it
