| From Variational to Deterministic Autoencoders P Ghosh, MSM Sajjadi, A Vergari, M Black, B Schölkopf Proceedings of the Eight International Conference on Learning …, 2020 | 311 | 2020 |
| Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning R Peharz, A Vergari, K Stelzner, A Molina, X Shao, M Trapp, K Kersting, ... Proceedings of UAI, 2019 | 156* | 2019 |
| Mixed sum-product networks: A deep architecture for hybrid domains A Molina, A Vergari, N Di Mauro, S Natarajan, F Esposito, K Kersting Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 128 | 2018 |
| Simplifying, regularizing and strengthening sum-product network structure learning A Vergari, N Di Mauro, F Esposito Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015 | 121 | 2015 |
| Einsum networks: Fast and scalable learning of tractable probabilistic circuits R Peharz, S Lang, A Vergari, K Stelzner, A Molina, M Trapp, ... International Conference on Machine Learning, 7563-7574, 2020 | 117 | 2020 |
| Probabilistic circuits: A unifying framework for tractable probabilistic models YJ Choi, A Vergari, G Van den Broeck Technical report, 2021 | 110* | 2021 |
| Semantic Probabilistic Layers for Neuro-Symbolic Learning K Ahmed, S Teso, KW Chang, GV Broeck, A Vergari NeurIPS 2022, 2022 | 61 | 2022 |
| SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks A Molina, A Vergari, K Stelzner, R Peharz, P Subramani, N Di Mauro, ... arXiv preprint arXiv:1901.03704, 2019 | 58 | 2019 |
| A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference A Vergari, YJ Choi, A Liu, S Teso, G Van den Broeck | 56* | 2021 |
| On tractable computation of expected predictions P Khosravi, YJ Choi, Y Liang, A Vergari, G Van den Broeck Advances in Neural Information Processing Systems, 11169-11180, 2019 | 52 | 2019 |
| Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures X Shao, A Molina, A Vergari, K Stelzner, R Peharz, T Liebig, K Kersting The 10th International Conference on Probabilistic Graphical Models, 2020 | 46 | 2020 |
| Visualizing and understanding sum-product networks A Vergari, N Di Mauro, F Esposito Machine Learning 108, 551-573, 2019 | 46 | 2019 |
| Automatic bayesian density analysis A Vergari, A Molina, R Peharz, Z Ghahramani, K Kersting, I Valera Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5207-5215, 2019 | 42 | 2019 |
| Strudel: Learning Structured-Decomposable Probabilistic Circuits M Dang, A Vergari, GV Broeck The 10th International Conference on Probabilistic Graphical Models, 2020 | 39 | 2020 |
| Handling Missing Data in Decision Trees: A Probabilistic Approach P Khosravi, A Vergari, YJ Choi, Y Liang, GV Broeck arXiv preprint arXiv:2006.16341, 2020 | 35 | 2020 |
| Probabilistic circuits: Representations, inference, learning and applications A Vergari, YJ Choi, R Peharz, G Van den Broeck AAAI Tutorial, 2020 | 33 | 2020 |
| End-to-end Learning of Deep Spatio-temporal Representations for Satellite Image Time Series Classification N Di Mauro, A Vergari, TMA Basile, FG Ventola, F Esposito Proceedings of the ECML/PKDD Discovery Challenges co-located with European …, 2017 | 33 | 2017 |
| Juice: A julia package for logic and probabilistic circuits M Dang, P Khosravi, Y Liang, A Vergari, G Van den Broeck Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 16020 …, 2021 | 29 | 2021 |
| Fast and accurate density estimation with extremely randomized cutset networks N Di Mauro, A Vergari, TMA Basile, F Esposito Machine Learning and Knowledge Discovery in Databases: European Conference …, 2017 | 29 | 2017 |
| Sum-product autoencoding: Encoding and decoding representations using sum-product networks A Vergari, R Peharz, N Di Mauro, A Molina, K Kersting, F Esposito Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 26 | 2018 |
Nicola Di MauroAssociate Professor of Computer Science, University of Bari在 uniba.it 的电子邮件经过验证
