Antonio Vergari
Title
Cited by
Cited by
Year
Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning
A Vergari, N Di Mauro, F Esposito
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015
792015
Mixed sum-product networks: A deep architecture for hybrid domains
A Molina, A Vergari, N Di Mauro, S Natarajan, F Esposito, K Kersting
Thirty-second AAAI conference on artificial intelligence, 2018
542018
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
44*2019
From Variational to Deterministic Autoencoders
P Ghosh, MSM Sajjadi, A Vergari, M Black, B Schölkopf
arXiv preprint arXiv:1903.12436, 2019
432019
Visualizing and understanding sum-product networks
A Vergari, N Di Mauro, F Esposito
Machine Learning 108 (4), 551-573, 2019
232019
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, 5207-5215, 2019
172019
Sum-product autoencoding: Encoding and decoding representations using sum-product networks
A Vergari, R Peharz, N Di Mauro, A Molina, K Kersting, F Esposito
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
172018
Learning Accurate Cutset Networks by Exploiting Decomposability
N Di Mauro, A Vergari, F Esposito
AI* IA 2015, Advances in Artificial Intelligence, 221-232, 2015
172015
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
152019
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
142017
Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks
N Di Mauro, A Vergari, TMA Basile, F Esposito
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017
132017
Learning Bayesian Random Cutset Forests
N Di Mauro, A Vergari, TMA Basile
International Symposium on Methodologies for Intelligent Systems, 122-132, 2015
132015
Multi-Label Classification with Cutset Networks
N Di Mauro, A Vergari, F Esposito
Proceedings of the Eighth International Conference on Probabilistic …, 2016
102016
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
R Peharz, S Lang, A Vergari, K Stelzner, A Molina, M Trapp, GV Broeck, ...
arXiv preprint arXiv:2004.06231, 2020
62020
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
X Shao, A Molina, A Vergari, K Stelzner, R Peharz, T Liebig, K Kersting
arXiv preprint arXiv:1905.08550, 2019
62019
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
62019
Density Estimators for Positive-Unlabeled Learning
TMA Basile, N Di Mauro, F Esposito, S Ferilli, A Vergari
International Workshop on New Frontiers in Mining Complex Patterns, 49-64, 2017
5*2017
Sum-Product Network structure learning by efficient product nodes discovery
N Di Mauro, F Esposito, FG Ventola, A Vergari
Intelligenza Artificiale 12 (2), 143-159, 2018
32018
Alternative Variable Splitting Methods to Learn Sum-Product Networks
N Di Mauro, F Esposito, FG Ventola, A Vergari
Conference of the Italian Association for Artificial Intelligence, 334-346, 2017
32017
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
22020
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Articles 1–20