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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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書(shū)目名稱(chēng)Neural-Symbolic Learning and Reasoning
副標(biāo)題18th International C
編輯Tarek R. Besold,Artur d’Avila Garcez,Benedikt Wagn
視頻videohttp://file.papertrans.cn/664/663768/663768.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Neural-Symbolic Learning and Reasoning; 18th International C Tarek R. Besold,Artur d’Avila Garcez,Benedikt Wagn Conference proceedings 2024
描述.This book constitutes the refereed proceedings of the 18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024, held in Barcelona, Spain during September 9-12th, 2024...The 30 full papers and 18 short papers were carefully reviewed and selected from 89 submissions, which presented the latest and ongoing research work on neurosymbolic AI.?Neurosymbolic AI aims to build rich computational models and systems by combining neural and symbolic learning and reasoning paradigms. This combination hopes to form synergies among their strengths while overcoming their.complementary weaknesses..
出版日期Conference proceedings 2024
關(guān)鍵詞Neurosymbolic Artificial Intelligence; Hybrid Learning and Reasoning Systems; Artificial intelligence;
版次1
doihttps://doi.org/10.1007/978-3-031-71167-1
isbn_softcover978-3-031-71166-4
isbn_ebook978-3-031-71167-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Bayesian Inverse Graphics for?Few-Shot Concept Learninguses our new differentiable renderer for optimizing global scene parameters through gradient descent, sampling posterior distributions over object parameters with Markov Chain Monte Carlo (MCMC), and using a neural based likelihood function. The code and datasets are available at .).
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Enhancing Machine Learning Predictions Through Knowledge Graph Embeddingsechniques, applied to heart and chronic kidney disease prediction. Our results indicate consistent improvements in model performance across various ML models and tasks, thus confirming our hypothesis, e.g. we increased the F2 score for the KNN from 70% to 82.22%, and the F2 score for SVM from 74.53%
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