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Antonio Vergari
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From Variational to Deterministic Autoencoders
P Ghosh, MSM Sajjadi, A Vergari, M Black, B Schölkopf
Proceedings of the Eight International Conference on Learning …, 2020
3172020
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
1282018
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
1212015
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
1172020
Probabilistic circuits: A unifying framework for tractable probabilistic models
YJ Choi, A Vergari, G Van den Broeck
Technical report, 2021
112*2021
Semantic Probabilistic Layers for Neuro-Symbolic Learning
K Ahmed, S Teso, KW Chang, GV Broeck, A Vergari
NeurIPS 2022, 2022
642022
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
582019
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
522019
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
462020
Visualizing and understanding sum-product networks
A Vergari, N Di Mauro, F Esposito
Machine Learning 108, 551-573, 2019
462019
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
422019
Strudel: Learning Structured-Decomposable Probabilistic Circuits
M Dang, A Vergari, GV Broeck
The 10th International Conference on Probabilistic Graphical Models, 2020
392020
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
352020
Probabilistic circuits: Representations, inference, learning and applications
A Vergari, YJ Choi, R Peharz, G Van den Broeck
AAAI Tutorial, 2020
332020
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
332017
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
292021
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
292017
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
262018
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