Titlebook: Neural-Symbolic Learning and Reasoning; 18th International C Tarek R. Besold,Artur d’Avila Garcez,Benedikt Wagn Conference proceedings 2024

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ULLER: A Unified Language for?Learning and?Reasoningnow are a wide variety of NeSy frameworks, each with its own specific language for expressing background knowledge and how to relate it to neural networks. This heterogeneity hinders accessibility for newcomers and makes comparing different NeSy frameworks challenging. We propose a unified language

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Disentangling Visual Priors: Unsupervised Learning of?Scene Interpretations with?Compositional Autoe transforms, and other higher-level structures. We propose a neurosymbolic architecture that uses a domain-specific language to capture selected priors of image formation, including object shape, appearance, categorization, and geometric transforms. We express template programs in that language and

LEER

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Enhancing Machine Learning Predictions Through Knowledge Graph Embeddingsby insufficient training data and poor data quality, with particularly severe consequences in critical areas such as medical diagnosis prediction. Our hypothesis is that enhancing ML pipelines with semantic information such as those available in knowledge graphs (KG) can address these challenges and

CIS

Terminating Differentiable Tree Expertsor Product Representations. We investigate the architecture and propose two key components. We first remove a series of different transformer layers that are used in every step by introducing a mixture of experts. This results in a Differentiable Tree Experts model with a constant number of paramete

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