Antonio Vergari
Tytuł
Cytowane przez
Cytowane przez
Rok
From Variational to Deterministic Autoencoders
P Ghosh, MSM Sajjadi, A Vergari, M Black, B Schölkopf
Proceedings of the Eight International Conference on Learning …, 2020
972020
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
912015
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
732018
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
63*2019
Visualizing and understanding sum-product networks
A Vergari, N Di Mauro, F Esposito
Machine Learning 108 (4), 551-573, 2019
272019
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
262019
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
262019
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
222019
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
202017
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
182018
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
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
152020
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
142020
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
142017
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
132020
Learning Bayesian Random Cutset Forests
N Di Mauro, A Vergari, TMA Basile
International Symposium on Methodologies for Intelligent Systems, 122-132, 2015
122015
Multi-Label Classification with Cutset Networks
N Di Mauro, A Vergari, F Esposito
Proceedings of the Eighth International Conference on Probabilistic …, 2016
112016
Strudel: Learning Structured-Decomposable Probabilistic Circuits
M Dang, A Vergari, GV Broeck
The 10th International Conference on Probabilistic Graphical Models, 2020
102020
Juice: A julia package for logic and probabilistic circuits
M Dang, P Khosravi, Y Liang, A Vergari, G Van den Broeck
Proceedings of the 35th AAAI Conference on Artificial Intelligence (Demo Track), 2021
92021
Probabilistic circuits: Representations, inference, learning and applications
A Vergari, YJ Choi, R Peharz, G Van den Broeck
Tutorial at the The 34th AAAI Conference on Artificial Intelligence, 2020
72020
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