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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书目名称Neural-Symbolic Learning and Reasoning影响因子(影响力)




书目名称Neural-Symbolic Learning and Reasoning影响因子(影响力)学科排名




书目名称Neural-Symbolic Learning and Reasoning网络公开度




书目名称Neural-Symbolic Learning and Reasoning网络公开度学科排名




书目名称Neural-Symbolic Learning and Reasoning被引频次




书目名称Neural-Symbolic Learning and Reasoning被引频次学科排名




书目名称Neural-Symbolic Learning and Reasoning年度引用




书目名称Neural-Symbolic Learning and Reasoning年度引用学科排名




书目名称Neural-Symbolic Learning and Reasoning读者反馈




书目名称Neural-Symbolic Learning and Reasoning读者反馈学科排名

Stricture

Insul岛

行乞

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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Ancestor

Recessive

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%

buoyant

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