Publications

Igor Cataneo Silveira, André Barbosa, Daniel Silva Lopes Da Costa, Denis D. Mauá (2025). Investigating Universal Adversarial Attacks Against Transformers-Based Automatic Essay Scoring Systems. Intelligent Systems.
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Renato Lui Geh, Jonas Gonçalves, Igor C. Silveira, Denis D. Mauá, Fabio G. Cozman (2024). dPASP: A Probabilistic Logic Programming Environment For Neurosymbolic Learning and Reasoning. Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024).
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Denis D. Mauá, Fabio Gagliardi Cozman, Alexandro Garces (2024). Probabilistic Logic Programming under the L-Stable Semantics. Proceedings of the 22nd International Workshop on Nonmonotonic Reasoning (NMR 2024).
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Igor Cataneo Silveira, André Barbosa, Denis D. Mauá (2024). A New Benchmark for Automatic Essay Scoring in Portuguese. Proceedings of the 16th International Conference on Computational Processing of Portuguese (PROPOR 2024).
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Benjie Wang, Denis D. Mauá, Guy Van Den Broeck, YooJung Choi (2024). A Compositional Atlas for Algebraic Circuits. Thirty-Eight Annual Conference on Neural Information Processing Systems (NeurIPS 2024).
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Denis D. Mauá, Fabio Gagliardi Cozman (2023). Specifying credal sets with probabilistic answer set programming. Proceedings of the Thirteenth International Symposium on Imprecise Probability: Theories and Applications (ISIPTA 2023).
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Tiago Madeira, Denis D. Mauá (2023). On Modal Clustering with Gaussian Sum-Product Networks. The 6th Workshop on Tractable Probabilistic Modeling.
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Victor Hugo Nascimento Rocha, Igor Cataneo Silveira, Paulo Pirozelli, Denis D. Mauá, Fabio Gagliardi Cozman (2023). Assessing Good, Bad and Ugly Arguments Generated by ChatGPT: a New Dataset, its Methodology and Associated Tasks. Progress in Artificial Intelligence.
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Tiago Madeira, Denis Mauá (2022). Tractable Mode-Finding in Sum-Product Networks with Gaussian Leaves. Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional.
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Julissa Villanueva, Denis Mauá (2022). Tractable Classification with Non-Ignorable Missing Data Using Generative Random Forests. Anais do X Symposium on Knowledge Discovery, Mining and Learning.
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