GI-IRlab-Artigos: Envíos recentes
Mostrando ítems 6-10 de 27
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Model Explanation via Support Graphs
(Cambridge Univeristy Press, 2024-02)[Absctract]: In this note, we introduce the notion of support graph to define explanations for any model of a logic program. An explanation is an acyclic support graph that, for each true atom in the model, induces a proof ... -
Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends
(Elsevier, 2023-12)[Abstract]: Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear ... -
M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines
(Elsevier B.V., 2020-11)[Abstract]: Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation development through its ability to analyze and continuously monitor large datasets. Fused deposition modeling (FDM) ... -
Equilibrium graphs
(Springer, 2019)[Abstract]: In this paper we present an extension of Peirce’s existential graphs to provide a diagrammatic representation of expressions in Quantified Equilibrium Logic (QEL). Using this formalisation, logical connectives ... -
Machine learning predicts 3D printing performance of over 900 drug delivery systems
(Elsevier B.V., 2021-09)[Abstract]: Three-dimensional printing (3DP) is a transformative technology that is advancing pharmaceutical research by producing personalized drug products. However, advances made via 3DP have been slow due to the lengthy ...