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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書目名稱Neural-Symbolic Learning and Reasoning影響因子(影響力)




書目名稱Neural-Symbolic Learning and Reasoning影響因子(影響力)學(xué)科排名




書目名稱Neural-Symbolic Learning and Reasoning網(wǎng)絡(luò)公開度




書目名稱Neural-Symbolic Learning and Reasoning網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Neural-Symbolic Learning and Reasoning被引頻次




書目名稱Neural-Symbolic Learning and Reasoning被引頻次學(xué)科排名




書目名稱Neural-Symbolic Learning and Reasoning年度引用




書目名稱Neural-Symbolic Learning and Reasoning年度引用學(xué)科排名




書目名稱Neural-Symbolic Learning and Reasoning讀者反饋




書目名稱Neural-Symbolic Learning and Reasoning讀者反饋學(xué)科排名

Stricture

Insul島

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 .).

Silent-Ischemia

模仿

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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