Antonio Vergari

873 total citations
22 papers, 142 citations indexed

About

Antonio Vergari is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Antonio Vergari has authored 22 papers receiving a total of 142 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Molecular Biology. Recurrent topics in Antonio Vergari's work include Bayesian Modeling and Causal Inference (7 papers), Machine Learning and Data Classification (6 papers) and Machine Learning and Algorithms (3 papers). Antonio Vergari is often cited by papers focused on Bayesian Modeling and Causal Inference (7 papers), Machine Learning and Data Classification (6 papers) and Machine Learning and Algorithms (3 papers). Antonio Vergari collaborates with scholars based in Italy, United Kingdom and United States. Antonio Vergari's co-authors include Nicola Di Mauro, Floriana Esposito, Alejandro Molina, Kristian Kersting, Guy Van den Broeck, Sriraam Natarajan, Michael J. Black, Mehdi S. M. Sajjadi, Bernhard Schölkopf and Partha Ghosh and has published in prestigious journals such as SHILAP Revista de lepidopterología, Machine Learning and Translational Psychiatry.

In The Last Decade

Antonio Vergari

20 papers receiving 134 citations

Peers

Antonio Vergari

CVPR

CNC

CTM

Vikash Sehwag United States

Joshua V. Dillon United States

Priscila Machado Vieira Lima Brazil

Yonglin Hao China

Prasoon Goyal India

Alexander Faithfull Denmark

Eleni Triantafillou Canada

Lingyuan Meng China

Ronghang Zhu United States

Guillaume Cleuziou France

Vikash Sehwag United States

Antonio Vergari

76 ×0.8

33 ×0.9

CVPR

20 ×1.1

11 ×0.7

CNC

2 ×0.1

CTM

Citations per year, relative to Antonio Vergari Antonio Vergari (= 1×) peers Vikash Sehwag

Countries citing papers authored by Antonio Vergari

Since Specialization

Citations

This map shows the geographic impact of Antonio Vergari's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Antonio Vergari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Antonio Vergari more than expected).

Fields of papers citing papers by Antonio Vergari

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Antonio Vergari. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Antonio Vergari. The network helps show where Antonio Vergari may publish in the future.

Co-authorship network of co-authors of Antonio Vergari

This figure shows the co-authorship network connecting the top 25 collaborators of Antonio Vergari. A scholar is included among the top collaborators of Antonio Vergari based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Antonio Vergari. Antonio Vergari is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Vergari, Antonio, et al.. (2024). PIXAR: Auto-Regressive Language Modeling in Pixel Space. Edinburgh Research Explorer (University of Edinburgh). 14673–14695.

Wulf, W., et al.. (2024). Knowledge graph embeddings in the biomedical domain: are they useful? A look at link prediction, rule learning, and downstream polypharmacy tasks. Bioinformatics Advances. 4(1). vbae097–vbae097. 3 indexed citations

Vergari, Antonio, et al.. (2024). Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification. Proceedings of the AAAI Conference on Artificial Intelligence. 38(11). 12208–12216. 1 indexed citations

Corponi, Filippo, Gerard Anmella, Isabella Pacchiarotti, et al.. (2024). A Bayesian analysis of heart rate variability changes over acute episodes of bipolar disorder. SHILAP Revista de lepidopterología. 3(1). 44–44. 1 indexed citations

Corponi, Filippo, Gerard Anmella, Isabella Pacchiarotti, et al.. (2024). Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number. Translational Psychiatry. 14(1). 161–161. 4 indexed citations

Japkowicz, Nathalie, et al.. (2024). From MNIST to ImageNet and back: benchmarking continual curriculum learning. Machine Learning. 113(10). 8137–8164. 6 indexed citations

Vergari, Antonio, et al.. (2021). A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference. Edinburgh Research Explorer. 34. 4 indexed citations

Liang, Yitao, et al.. (2021). Juice: A Julia Package for Logic and Probabilistic Circuits. Proceedings of the AAAI Conference on Artificial Intelligence. 35(18). 16020–16023. 7 indexed citations

Molina, Alejandro, et al.. (2021). Conditional sum-product networks: Modular probabilistic circuits via gate functions. International Journal of Approximate Reasoning. 140. 298–313. 2 indexed citations

10.

Vergari, Antonio, et al.. (2021). Strudel: A fast and accurate learner of structured-decomposable probabilistic circuits. International Journal of Approximate Reasoning. 140. 92–115. 2 indexed citations

11.

Vergari, Antonio, et al.. (2020). Strudel: Learning Structured-Decomposable Probabilistic Circuits.. 137–148. 5 indexed citations

12.

Ghosh, Partha, Mehdi S. M. Sajjadi, Antonio Vergari, Michael J. Black, & Bernhard Schölkopf. (2020). From Variational to Deterministic Autoencoders. International Conference on Learning Representations. 23 indexed citations

13.

Zeng, Zhe, et al.. (2020). Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations. Lirias (KU Leuven). 33. 11564–11575. 3 indexed citations

14.

Liang, Yitao, et al.. (2019). On Tractable Computation of Expected Predictions. arXiv (Cornell University). 32. 11167–11178. 7 indexed citations

15.

Peharz, Robert, Antonio Vergari, Alejandro Molina, et al.. (2019). Random sum-product networks: A simple and effective approach to probabilistic deep learning. TU/e Research Portal. 334–344. 14 indexed citations

16.

Basile, Teresa M. A., Nicola Di Mauro, Floriana Esposito, Stefano Ferilli, & Antonio Vergari. (2019). Ensembles of density estimators for positive-unlabeled learning. Journal of Intelligent Information Systems. 53(2). 199–217. 6 indexed citations

17.

Molina, Alejandro, Antonio Vergari, Nicola Di Mauro, et al.. (2018). Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 35 indexed citations

18.

Vergari, Antonio, Robert Peharz, Nicola Di Mauro, et al.. (2018). Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 6 indexed citations

19.

Mauro, Nicola Di, et al.. (2017). End-to-end Learning of Deep Spatio-temporal Representations for Satellite Image Time Series Classification.. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 10 indexed citations

20.

Mauro, Nicola Di, Antonio Vergari, & Floriana Esposito. (2016). Multi-Label Classification with Cutset Networks. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 147–158. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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