Hermite-Fisher bounds and stability for min-entropy power inequalities
通过结合变分原理和适当正交化的Hermite多项式测试函数,本文推导出相对Fisher信息的显式下界,并建立了所有维度下的最小熵幂不等式的量化版本。
通过结合变分原理和适当正交化的Hermite多项式测试函数,本文推导出相对Fisher信息的显式下界,并建立了所有维度下的最小熵幂不等式的量化版本。
研究对比六种最新模型在图神经网络中的反事实解释方法,旨在通过添加或删除边来最小化修改以改变预测结果,评估其性能以指导未来研究。
本文针对含有结构性零值的组合数据,提出了一种条件对数正态分布模型,并通过EM算法实现快速计算。
研究通过排除年龄因素,使用声学模型在COPD筛查中分离出非年龄相关的声学信号,以解决因年龄相关的声音变化对COPD筛查结果的影响。
This study addresses the scarcity of structured computational resources for non-standard Greek slang—a linguistic variety hindered by its dynamic and unregulated nature, which impedes both linguistic inquiry and NLP applications. To bridge this gap, the authors present slang.gr, the first large-scale computational resource for Greek slang, integrating lexical entries, user-generated tags, and interaction data. They introduce a novel multi-layer taxonomy that uniquely combines semantic and sociolinguistic metadata. Through folksonomy-based tag cleaning, ontology mapping, community behavior modeling, and a credibility-weighted scoring algorithm, the work reveals that Greek slang is highly oriented toward person- and evaluation-related expressions and exhibits strong morphological innovativeness. The proposed taxonomy significantly enhances analytical interpretability and establishes a foundational framework for computational modeling of non-standard language varieties.
通过结合变分原理和适当正交化的Hermite多项式测试函数,本文推导出相对Fisher信息的显式下界,并建立了所有维度下的最小熵幂不等式的量化版本。
研究对比六种最新模型在图神经网络中的反事实解释方法,旨在通过添加或删除边来最小化修改以改变预测结果,评估其性能以指导未来研究。
本文针对含有结构性零值的组合数据,提出了一种条件对数正态分布模型,并通过EM算法实现快速计算。
研究通过排除年龄因素,使用声学模型在COPD筛查中分离出非年龄相关的声学信号,以解决因年龄相关的声音变化对COPD筛查结果的影响。
This study addresses the scarcity of structured computational resources for non-standard Greek slang—a linguistic variety hindered by its dynamic and unregulated nature, which impedes both linguistic inquiry and NLP applications. To bridge this gap, the authors present slang.gr, the first large-scale computational resource for Greek slang, integrating lexical entries, user-generated tags, and interaction data. They introduce a novel multi-layer taxonomy that uniquely combines semantic and sociolinguistic metadata. Through folksonomy-based tag cleaning, ontology mapping, community behavior modeling, and a credibility-weighted scoring algorithm, the work reveals that Greek slang is highly oriented toward person- and evaluation-related expressions and exhibits strong morphological innovativeness. The proposed taxonomy significantly enhances analytical interpretability and establishes a foundational framework for computational modeling of non-standard language varieties.