| A Fair Comparison of Graph Neural Networks for Graph Classification F Errica, M Podda, D Bacciu, A Micheli 8th International Conference on Learning Representations (ICLR), 2020, 2020 | 524 | 2020 |
| A Gentle Introduction to Deep Learning for Graphs D Bacciu, F Errica, A Micheli, M Podda Neural Networks 129 (September), 203-221, 2020 | 327 | 2020 |
| Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing D Bacciu, F Errica, A Micheli Proceedings of the 35th International Conference on Machine Learning (ICML …, 2018 | 98 | 2018 |
| Towards learning trustworthily, automatically, and with guarantees on graphs: An overview L Oneto, N Navarin, B Biggio, F Errica, A Micheli, F Scarselli, M Bianchini, ... Neurocomputing 493, 217-243, 2022 | 26 | 2022 |
| Graph Mixture Density Networks F Errica, D Bacciu, A Micheli Proceedings of the 38th International Conference on Machine Learning (ICML …, 2021 | 26 | 2021 |
| Probabilistic Learning on Graphs via Contextual Architectures D Bacciu, F Errica, A Micheli Journal of Machine Learning Research (JMLR) 21 (134), 1-39, 2020 | 19 | 2020 |
| Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification A Carta, A Cossu, F Errica, D Bacciu WWW'21 Workshop on Graph Learning Benchmarks, 2021 | 18 | 2021 |
| A deep graph network-enhanced sampling approach to efficiently explore the space of reduced representations of proteins F Errica, M Giulini, D Bacciu, R Menichetti, A Micheli, R Potestio Frontiers in Molecular Biosciences 8, 136, 2021 | 15 | 2021 |
| Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials V Zaverkin, D Holzmüller, H Christiansen, F Errica, F Alesiani, ... | 8 | 2023 |
| Modeling Edge Features with Deep Bayesian Graph Networks D Atzeni, D Bacciu, F Errica, A Micheli 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 8 | 2021 |
| The Infinite Contextual Graph Markov Model D Castellana, F Errica, D Bacciu, A Micheli International Conference on Machine Learning (ICML 2022), 2022 | 7 | 2022 |
| Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark A Carta, A Cossu, F Errica, D Bacciu Frontiers in Artificial Intelligence 5, 2022 | 7 | 2022 |
| Theoretically Expressive and Edge-aware Graph Learning F Errica, D Bacciu, A Micheli 28th European Symposium on Artificial Neural Networks, Computational …, 2020 | 6 | 2020 |
| FA3L at SemEval-2017 task 3: A three embeddings recurrent neural network for question answering G Attardi, A Carta, F Errica, A Madotto, L Pannitto Proceedings of the 11th International Workshop on Semantic Evaluation …, 2017 | 5 | 2017 |
| Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks F Errica, M Niepert International Conference on Learning Representations (ICLR 2024), 2024 | 4 | 2024 |
| Self-tuning Hamiltonian Monte Carlo for accelerated sampling H Christiansen, F Errica, F Alesiani The Journal of Chemical Physics 159 (23), 2023 | 4 | 2023 |
| Hidden Markov Models for Temporal Graph Representation Learning F Errica, A Gravina, D Bacciu, A Micheli Proceedings of the European Symposium on Artificial Neural Networks …, 2023 | 4 | 2023 |
| Deep Graph Networks for Drug Repurposing With Multi-Protein Targets D Bacciu, F Errica, A Gravina, L Madeddu, M Podda, G Stilo IEEE Transactions on Emerging Topics in Computing 12 (1), 177-189, 2023 | 4 | 2023 |
| Robust Malware Classification via Deep Graph Networks on Call Graph Topologies F Errica, G Iadarola, F Martinelli, F Mercaldo, A Micheli European Symposium on Artificial Neural Networks (ESANN), 2021 | 4 | 2021 |
| Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching F Errica, H Christiansen, V Zaverkin, T Maruyama, M Niepert, F Alesiani arXiv preprint arXiv:2312.16560, 2023 | 3 | 2023 |
Davide BacciuDipartimento di Informatica, Università di PisaEmail verificata su di.unipi.it
