Antonio Vergari

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	Toutes	Depuis 2016
Citations	513	512
indice h	13	13
indice i10	17	17

240

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180

20162017201820192020202115 36 52 135 233 35

Coauteurs

Nicola Di MauroAssistant Professor of Computer Science, University of BariAdresse e-mail validée de uniba.it
Floriana EspositoProfessore di Informatica, Università degli Studi "Aldo Moro" Bari ItalyAdresse e-mail validée de uniba.it
Kristian KerstingProfessor of Artificial Intelligence and Machine Learning, hessian.ai, TU DarmstadtAdresse e-mail validée de cs.tu-darmstadt.de
Alejandro MolinaTU DarmstadtAdresse e-mail validée de cs.tu-darmstadt.de
Robert PeharzAssistant Professor of AI, TU EindhovenAdresse e-mail validée de tue.nl
Guy Van den BroeckAssociate Professor and Samueli Fellow, UCLAAdresse e-mail validée de cs.ucla.edu
Karl StelznerTU DarmstadtAdresse e-mail validée de cs.tu-darmstadt.de
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleAdresse e-mail validée de eng.cam.ac.uk
Teresa Maria Altomare BasileRicercatore, Università degli Studi di BariAdresse e-mail validée de uniba.it
Martin TrappMachine learning researcher, Aalto UniversityAdresse e-mail validée de aalto.fi
YooJung ChoiPhD Student, UCLAAdresse e-mail validée de cs.ucla.edu
Sriraam NatarajanUniversity of Texas at DallasAdresse e-mail validée de utdallas.edu
Pasha KhosraviComputer Science PhD Student at UCLAAdresse e-mail validée de cs.ucla.edu
Yitao LiangPh.D Student of Computer Science, UCLAAdresse e-mail validée de cs.ucla.edu
Isabel ValeraSaarland University and Max Planck Institute for Intelligent SystemsAdresse e-mail validée de tuebingen.mpg.de
Xiaoting ShaoTU DarmstadtAdresse e-mail validée de cs.tu-darmstadt.de
Michael J. BlackMax Planck Institute for Intelligent Systems and AmazonAdresse e-mail validée de tuebingen.mpg.de
Mehdi S. M. SajjadiGoogle BrainAdresse e-mail validée de google.com
Partha GhoshPh.D. Student, Doctoral Fellow at the IMPRS-ISAdresse e-mail validée de tue.mpg.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedAdresse e-mail validée de tuebingen.mpg.de

Antonio Vergari

PostDoc, University of California, Los Angeles

Adresse e-mail validée de cs.ucla.edu - Page d'accueil

Artificial Intelligence Machine Learning Probabilistic Learning Deep Learning Tractable Probabilistic Modeling


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	89	2015
From Variational to Deterministic Autoencoders P Ghosh, MSM Sajjadi, A Vergari, M Black, B Schölkopf Proceedings of the Eight International Conference on Learning …, 2020	66	2020
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	63	2018
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	55*	2019
Visualizing and understanding sum-product networks A Vergari, N Di Mauro, F Esposito Machine Learning 108 (4), 551-573, 2019	26	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	22	2019
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	20	2019
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	17	2018
Learning Accurate Cutset Networks by Exploiting Decomposability N Di Mauro, A Vergari, F Esposito AI* IA 2015, Advances in Artificial Intelligence, 221-232, 2015	17	2015
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	16	2019
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	16	2017
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	14	2017
Learning Bayesian Random Cutset Forests N Di Mauro, A Vergari, TMA Basile International Symposium on Methodologies for Intelligent Systems, 122-132, 2015	13	2015
Multi-Label Classification with Cutset Networks N Di Mauro, A Vergari, F Esposito Proceedings of the Eighth International Conference on Probabilistic …, 2016	11	2016
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	10	2020
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	10	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	10	2020
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	6	2020
Tractable probabilistic models: Representations, algorithms, learning, and applications G Van den Broeck, N Di Mauro, A Vergari Tutorial at UAI, 2019	5	2019
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

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