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

Pandemic

Ovulation

On the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysistional Neural Network. Our approach uses a Wikipedia-derived concept hierarchy with approx. 2 million classes as background knowledge, and deductive reasoning based Concept Induction for explanation generation. Additionally, we explore and compare the capabilities of off-the-shelf pre-trained multim

细胞膜

Concept Induction Using LLMs: A User Experiment for Assessmentraditional post-hoc algorithms, while useful, often struggle to deliver interpretable explanations. Concept-based models offer a promising avenue by incorporating explicit representations of concepts to enhance interpretability. However, existing research on automatic concept discovery methods is of

Fester

Error-Margin Analysis for Hidden Neuron Activation Labelsence. While existing literature in explainable AI emphasizes the importance of labeling neurons with concepts to understand their functioning, they mostly focus on identifying what stimulus activates a neuron in most cases; this corresponds to the notion of . in information retrieval. We argue that

Kernel

LENs for Analyzing the Quality of Life of People with Intellectual Disabilityintellectual disability and uses a framework in the literature of neurosymbolic AI, specifically the family of interpretable DL named logic explained networks, to provide explanations for the predictions. By integrating explainability, our research enhances the richness of the predictions and qualit

vitreous-humor

ECATS: Explainable-by-Design Concept-Based Anomaly Detection for Time Seriestion. However, the complexity inherent in Cyber Physical Systems (CPS) creates a challenge when it comes to explainability methods. To overcome this inherent lack of interpretability, we propose ECATS, a concept-based neuro-symbolic architecture where concepts are represented as Signal Temporal Logi

dura-mater

amygdala

outrage

e writing of the present book: Almost every topic that we taughtrequiredsomeskillsinalgebra,andinparticular,computeral- bra! From positioning to transformation problems inherent in geodesy and geoinformatics, knowledge of algebra and application of computer algebra software were required. In prepari

AGATE

Conference proceedings 2024celona, 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 n