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
772015
Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains.
A Molina, A Vergari, N Di Mauro, S Natarajan, F Esposito, K Kersting
AAAI, 3828-3835, 2018
492018
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
38*2019
From Variational to Deterministic Autoencoders
P Ghosh, MSM Sajjadi, A Vergari, M Black, B Schölkopf
arXiv preprint arXiv:1903.12436, 2019
362019
Visualizing and understanding sum-product networks
A Vergari, N Di Mauro, F Esposito
Machine Learning 108 (4), 551-573, 2019
222019
Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks.
A Vergari, R Peharz, N Di Mauro, A Molina, K Kersting, F Esposito
AAAI, 4163-4170, 2018
162018
Learning Accurate Cutset Networks by Exploiting Decomposability
N Di Mauro, A Vergari, F Esposito
AI* IA 2015, Advances in Artificial Intelligence, 221-232, 2015
162015
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
132019
Learning Bayesian Random Cutset Forests
N Di Mauro, A Vergari, TMA Basile
International Symposium on Methodologies for Intelligent Systems, 122-132, 2015
122015
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
112019
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
112017
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
102017
Multi-Label Classification with Cutset Networks
N Di Mauro, A Vergari, F Esposito
Proceedings of the Eighth International Conference on Probabilistic …, 2016
102016
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
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
52019
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
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
42020
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
Bayesian Nonparametric Hawkes Processes
J Kapoor, A Vergari, MG Rodriguez, I Valera
All of Bayesian Nonparametrics: Especially the Useful Bits (BNP@ NeurIPS 2018), 2018
22018
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
22018
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Articles 1–20