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Bernardo Cuenca

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Articles by Bernardo Cuenca

Computer Science > Artificial Intelligence

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By Przemysław Andrzej, Bernardo Cuenca
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Computer Science > Machine Learning

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By Bernardo Cuenca, İsmail İlkan
| arXiv
[Submitted on 21 May 2026] Title:What are the Right Symmetries for Formal Theorem Proving?
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[2602.13910] Sufficient Conditions for Stability of Minimum-Norm Interpolating Deep ReLU Networks

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By Ouns El, Bernardo Cuenca
| arXiv
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From Neural Networks to Logical Theories: The Correspondence between Fibring Modal Logics and Fibring Neural Networks

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By Ouns El, Bernardo Cuenca
| arXiv
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[2508.14091] Logical Expressivity and Explanations for Monotonic GNNs with Scoring Functions

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By David Tena, Bernardo Cuenca
| arXiv
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[2505.08021] The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic

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By Bernardo Cuenca
| arXiv
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[2408.10261] Relational Graph Convolutional Networks Do Not Learn Sound Rules

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By David Tena, Bernardo Cuenca
| arXiv
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[2401.02869] Practical Reasoning in DatalogMTL

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By Bernardo Cuenca
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2305.18015] On the Correspondence Between Monotonic Max-Sum GNNs and Datalog

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By Bernardo Cuenca, David Tena
| arXiv
[Submitted on 29 May 2023 ( v1 ), last revised 15 Jun 2023 (this version, v3)] Title:On the Correspondence Between Monotonic Max-Sum GNNs and Datalog Download a PDF of the paper titled On the Correspondence Between Monotonic Max-Sum GNNs and Datalog, by David Tena Cucala and 3 other authors Download PDF Abstract: Although there has been significant interest in applying machine learning techniques to structured data, the expressivity (i.e., a description of what can be learned) of such...
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Double-descent curves in neural networks: a new perspective using Gaussian processes

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By Ouns El, Bernardo Cuenca
| arXiv
[Submitted on 14 Feb 2021 ( v1 ), last revised 25 May 2023 (this version, v5)] Title:Double-descent curves in neural networks: a new perspective using Gaussian processes Download a PDF of the paper titled Double-descent curves in neural networks: a new perspective using Gaussian processes, by Ouns El Harzli and 2 other authors Download PDF Abstract: Double-descent curves in neural networks describe the phenomenon that the generalisation error initially descends with increasing parameters,...
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[2205.09901] Cardinality-Minimal Explanations for Monotonic Neural Networks

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By Ouns El, Bernardo Cuenca
| arXiv
[Submitted on 19 May 2022 ( v1 ), last revised 2 May 2023 (this version, v3)] Title:Cardinality-Minimal Explanations for Monotonic Neural Networks Download a PDF of the paper titled Cardinality-Minimal Explanations for Monotonic Neural Networks, by Ouns El Harzli and 2 other authors Download PDF Abstract: In recent years, there has been increasing interest in explanation methods for neural model predictions that offer precise formal guarantees.
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[2206.00362] An Empirical Study of Retrieval-enhanced Graph Neural Networks

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By Bernardo Cuenca
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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