瘦削
发表于 2025-3-21 16:56:31
书目名称Neural-Symbolic Learning and Reasoning影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0663767<br><br> <br><br>书目名称Neural-Symbolic Learning and Reasoning影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0663767<br><br> <br><br>书目名称Neural-Symbolic Learning and Reasoning网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0663767<br><br> <br><br>书目名称Neural-Symbolic Learning and Reasoning网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0663767<br><br> <br><br>书目名称Neural-Symbolic Learning and Reasoning被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0663767<br><br> <br><br>书目名称Neural-Symbolic Learning and Reasoning被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0663767<br><br> <br><br>书目名称Neural-Symbolic Learning and Reasoning年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0663767<br><br> <br><br>书目名称Neural-Symbolic Learning and Reasoning年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0663767<br><br> <br><br>书目名称Neural-Symbolic Learning and Reasoning读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0663767<br><br> <br><br>书目名称Neural-Symbolic Learning and Reasoning读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0663767<br><br> <br><br>
脆弱吧
发表于 2025-3-21 23:16:11
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BILE
发表于 2025-3-22 02:16:09
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沟通
发表于 2025-3-22 08:06:03
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不愿
发表于 2025-3-22 09:17:18
Towards Understanding the Impact of Graph Structure on Knowledge Graph Embeddingsthodologies for producing KGs, which span notions of expressivity, and are tailored for different use-cases and domains. Now, as neurosymbolic methods rise in prominence, it is important to understand how the development of KGs according to these methodologies impact downstream tasks, such as link p
刺耳
发表于 2025-3-22 14:49:34
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mortuary
发表于 2025-3-22 19:49:27
Metacognitive AI: Framework and the Case for a Neurosymbolic Approachgy. In this position paper, we examine the concept of applying metacognition to artificial intelligence. We introduce a framework for understanding metacognitive artificial intelligence (AI) that we call TRAP: transparency, reasoning, adaptation, and perception. We discuss each of these aspects in-t
Maximize
发表于 2025-3-22 21:56:16
Enhancing Logical Tensor Networks: Integrating Uninorm-Based Fuzzy Operators for Complex Reasoning between t-norms and t-conorms, offer unparalleled flexibility and adaptability, making them ideal for modeling the complex, often ambiguous relationships inherent in real-world data. By embedding these operators into Logic Tensor Networks, we present a methodology that significantly increases the n
脊椎动物
发表于 2025-3-23 03:37:07
Parameter Learning Using Approximate Model Counting these hybrid models, these methods use a knowledge compiler to turn the symbolic model into a differentiable arithmetic circuit, after which gradient descent can be performed. However, these methods require compiling a reasonably sized circuit, which is not always possible, as for many symbolic pro
分开
发表于 2025-3-23 08:51:35
Large-Scale Knowledge Integration for Enhanced Molecular Property Predictionitical for advancements in drug discovery and materials science. While recent work has primarily focused on data-driven approaches, the KANO model introduces a novel paradigm by incorporating knowledge-enhanced pre-training. In this work, we expand upon KANO by integrating the large-scale ChEBI know