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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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樓主: 瘦削
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發(fā)表于 2025-3-23 12:01:39 | 只看該作者
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發(fā)表于 2025-3-23 16:13:34 | 只看該作者
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
13#
發(fā)表于 2025-3-23 22:00:21 | 只看該作者
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
14#
發(fā)表于 2025-3-24 01:25:12 | 只看該作者
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
15#
發(fā)表于 2025-3-24 03:49:40 | 只看該作者
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
16#
發(fā)表于 2025-3-24 08:08:14 | 只看該作者
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
17#
發(fā)表于 2025-3-24 14:21:13 | 只看該作者
18#
發(fā)表于 2025-3-24 14:56:13 | 只看該作者
19#
發(fā)表于 2025-3-24 21:45:45 | 只看該作者
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
20#
發(fā)表于 2025-3-25 02:12:21 | 只看該作者
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
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