Antonio Vergari

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Nicola Di MauroAssociate Professor of Computer Science, University of BariVerified email at uniba.it
Floriana EspositoProfessore di Informatica, Università degli Studi "Aldo Moro" Bari ItalyVerified email at uniba.it
Kristian KerstingProfessor of AI & ML, TU Darmstadt, Co-Director hessian.ai, DFKI, Germany, CLAIRE & ELLISVerified email at cs.tu-darmstadt.de
Guy Van den BroeckAssociate Professor and Samueli Fellow, UCLAVerified email at cs.ucla.edu
Robert PeharzAssistant Professor of AI, TU GrazVerified email at tugraz.at
Alejandro MolinaAmazonVerified email at amazon.com
Karl StelznerTU DarmstadtVerified email at cs.tu-darmstadt.de
YooJung ChoiAssistant Professor, Arizona State UniversityVerified email at asu.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Martin TrappMachine learning researcher, Aalto UniversityVerified email at aalto.fi
Teresa Maria Altomare BasileRicercatore, Università degli Studi di BariVerified email at uniba.it
Pasha KhosraviComputer Science PhD Student at UCLAVerified email at cs.ucla.edu
Xiaoting ShaoTU DarmstadtVerified email at cs.tu-darmstadt.de
Zhe ZengUniversity of California, Los AngelesVerified email at cs.ucla.edu
Yitao LiangAssistant Professor, Peking UniversityVerified email at pku.edu.cn
Sriraam NatarajanUniversity of Texas at DallasVerified email at utdallas.edu
Michael J. BlackMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Mehdi S. M. SajjadiGoogle BrainVerified email at google.com
Partha GhoshPh.D. Student, Doctoral Fellow at the IMPRS-ISVerified email at tue.mpg.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de

Antonio Vergari

Lecturer, University of Edinburgh, UK

Verified email at ed.ac.uk - Homepage

Artificial Intelligence Machine Learning Probabilistic Learning Deep Learning Tractable Probabilistic Modeling


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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	221	2020
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	112*	2019
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	109	2015
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	101	2018
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	61	2020
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	46	2019
Probabilistic circuits: A unifying framework for tractable probabilistic models YJ Choi, A Vergari, G Van den Broeck Technical report, 2021	42*	2021
Visualizing and understanding sum-product networks A Vergari, N Di Mauro, F Esposito Machine Learning 108 (4), 551-573, 2019	37	2019
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	36	2019
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	34	2020
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	32	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	26	2017
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	25	2020
Strudel: Learning Structured-Decomposable Probabilistic Circuits M Dang, A Vergari, GV Broeck The 10th International Conference on Probabilistic Graphical Models, 2020	24	2020
Learning Accurate Cutset Networks by Exploiting Decomposability N Di Mauro, A Vergari, F Esposito AI* IA 2015, Advances in Artificial Intelligence, 221-232, 2015	21	2015
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	19	2020
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	19	2018
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference A Vergari, YJ Choi, A Liu, S Teso, G Van den Broeck	18*	2021
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	18	2021
Fast and accurate density estimation with extremely randomized cutset networks N Di Mauro, A Vergari, T Basile, F Esposito Joint European conference on machine learning and knowledge discovery in …, 2017	18	2017

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