Antonio Vergari

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Nicola Di MauroAssociate Professor of Computer Science, University of BariVerified email at uniba.it
Guy Van den BroeckProfessor and Samueli Fellow, UCLAVerified email at cs.ucla.edu
Robert PeharzAssistant Professor of AI, TU GrazVerified email at tugraz.at
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
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
Stefano TesoSenior Assistant Professor, University of TrentoVerified email at unitn.it
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
Pasha KhosraviResearcher at LinkedInVerified email at linkedin.com
Zhe ZengUniversity of California, Los AngelesVerified email at cs.ucla.edu
Paolo MorettinAssistant professor (RTD-A), University of TrentoVerified email at unitn.it
Yitao LiangPeking UniversityVerified email at pku.edu.cn
Michael J. BlackMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Mehdi S. M. SajjadiResearch Scientist, Google DeepMindVerified email at google.com
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Meihua DangStudent, Stanford UniversityVerified email at cs.stanford.edu
Sriraam NatarajanUniversity of Texas at DallasVerified email at utdallas.edu

Antonio Vergari

Reader (Associate Professor), University of Edinburgh, UK

Verified email at ed.ac.uk - Homepage

Artificial Intelligence Probabilistic Machine Learning Probabilistic Circuits Neuro-Symbolic AI


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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	329	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	160*	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	133	2018
Probabilistic circuits: A unifying framework for tractable probabilistic models YJ Choi, A Vergari, G Van den Broeck Technical report, 2021	124*	2021
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	123	2020
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	122	2015
Semantic Probabilistic Layers for Neuro-Symbolic Learning K Ahmed, S Teso, KW Chang, GV Broeck, A Vergari NeurIPS 2022, 2022	75	2022
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference A Vergari, YJ Choi, A Liu, S Teso, G Van den Broeck	63*	2021
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	63	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	54	2019
Visualizing and understanding sum-product networks A Vergari, N Di Mauro, F Esposito Machine Learning 108, 551-573, 2019	50	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	47	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	42	2019
Strudel: Learning Structured-Decomposable Probabilistic Circuits M Dang, A Vergari, GV Broeck The 10th International Conference on Probabilistic Graphical Models, 2020	39	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	37	2020
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	35	2017
Probabilistic circuits: Representations, inference, learning and applications A Vergari, YJ Choi, R Peharz, G Van den Broeck AAAI Tutorial, 2020	34	2020
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	30	2021
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	30	2017
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	27	2018

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