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Topic: explainable ai

Unified directory of all items matching this research classification.
12 Linked Entries
Laboratory Members (2)
MJ

Milagros Jacinto

Estudiante de grado
#artificial intelligence
#explainable ai
#logic
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MM

Martin Moschettoni

Estudiante de posgrado
#artificial intelligence
#explainable ai
#logic
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Research Projects (1)

Técnicas de Inteligencia Artificial Neuro-simbólica

Director: Claudia Pons
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Academic Theses (4)

Abordando el problema de la explicablilidad en redes neuronales artificiales mediante lógica simbólica y algoritmos genéticos

PhD | Student: Gabriela Pérez
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Inteligencia Artificial Explicable: Análisis de Metodologías y Aplicaciones

Specialization | Student: María Cecilia Pezzini
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Inteligencia Artificial Explicable: Técnicas de extracción de reglas en redes neuronales artificiales

Undergraduate | Student: Milagros Jacinto y Martín Moschettoni
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Técnicas de Inteligencia artificial explicable basadas en una integración de lógica simbólica y no-simbólica

PhD | Student: Pablo Negro
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Research Scholarships (1)

Explicabilidad de redes neuronales

Beca de experiencia laboral | Student: Jeremías Salsamendi
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Scientific Bibliography (4)

Explainable Artificial Intelligence: Analysis of Methodologies and Applications

y Claudia Pons, M. C. P. (2025). Explainable Artificial Intelligence: Analysis of Methodologies and Applications. Journal of Computer Science & Technology, 25(2), 75–86. https://doi.org/https://doi.org/10.24215/16666038.25.e07
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Explicabilidad en algoritmos de búsqueda cuántica en hipercubo con valores de Shapley

Pezzini, M. C., Pons, C., & Bibbó, L. M. (2025). Explicabilidad en algoritmos de búsqueda cuántica en hipercubo con valores de Shapley. Revista Abierta de Informática Aplicada, 9(1), 169–192. https://doi.org/10.59471/raia2025224
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Pablo Negro and Claudia Pons (2024). “Rule Extraction in Trained Feedforward Deep Neural Networks: Integrating Cosine Similarity and Logic for Explainability”. International Journal of Artificial Intelligence and Machine Learning (IJAIML). ISSN: 2642-1577|EISSN: 2642-1585| https://doi.org/10.4018/IJAIML.347988 Volume 13, Issue 1, Article 2.

Pablo Negro and Claudia Pons (2024). “Rule Extraction in Trained Feedforward Deep Neural Networks: Integrating Cosine Similarity and Logic for Explainability”. International Journal of Artificial Intelligence and Machine Learning (IJAIML). ISSN: 2642-1577|EISSN: 2642-1585| https://doi.org/10.4018/IJAIML.347988 Volume 13, Issue 1, Article 2.
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Pablo Negro and Claudia Pons (2024). Extracting rules from trained feedforward neural networks with first order logic. Electronic Journal of SADIO (EJS), 23(1). pg.58-80. June 2024.https://doi.org/10.24215/15146774e040

Pablo Negro and Claudia Pons (2024). Extracting rules from trained feedforward neural networks with first order logic. Electronic Journal of SADIO (EJS), 23(1). pg.58-80. June 2024.https://doi.org/10.24215/15146774e040
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