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

小卒

Francesco Manigrasso,Stefan Schouten,Lia Morra,Peter Bloem

一回合

jabber

Tarek R. Besold,Artur d’Avila Garcez,Benedikt Wagn

温顺

ULLER: A Unified Language for Learning and Reasoningng classical FOL, fuzzy logic, and probabilistic logic. We believe . is a first step towards making NeSy research more accessible and comparable, paving the way for libraries that streamline training and evaluation across a multitude of semantics, knowledge bases, and NeSy systems.

生气地

Disentangling Visual Priors: Unsupervised Learning of Scene Interpretations with Compositional Autoeonfront our approach with a baseline method on a synthetic benchmark and demonstrate its capacity to disentangle selected aspects of the image formation process, learn from small data, correct inference in the presence of noise, and out-of-sample generalization.

Discrete

Terminating Differentiable Tree Experts to make automatically. The resulting Terminating Differentiable Tree Experts model sluggishly learns to predict the number of steps without an oracle. It can do so while maintaining the learning capabilities of the model, converging to the optimal amount of steps.

APNEA

BUOY

CRUC

0302-9743 ombining neural and symbolic learning and reasoning paradigms. This combination hopes to form synergies among their strengths while overcoming their.complementary weaknesses..978-3-031-71166-4978-3-031-71167-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

FACET