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相关概念视频

Natural and Artificial Concepts01:24

Natural and Artificial Concepts

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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Concepts and Prototypes01:24

Concepts and Prototypes

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The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Schemata01:17

Schemata

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A schema is a mental construct that organizes related concepts, allowing the brain to process information efficiently. Upon activation, schemata facilitate assumptions about people or objects.
Two types of schemata are:
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Data: Types and Distribution01:19

Data: Types and Distribution

838
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
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Language and Cognition01:27

Language and Cognition

438
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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相关实验视频

Updated: Sep 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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扩展本体学的框嵌入:一个数据驱动和可解释的方法

Adel Memariani1, Martin Glauer2, Simon Flügel3

  • 1Data Science Group (DICE), Heinz Nixdorf Institute, Paderborn University, Warburger Str. 100, 33098, Paderborn, North Rhine-Westphalia, Germany. adel.memariani@uni-paderborn.de.

Journal of cheminformatics
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概括
此摘要是机器生成的。

本研究引入了一种用于多标签分类的可解释深度学习的新方法,通过使用盒形嵌入来表示层次关系. 该方法实现了最先进的性能,同时确保与本体概念的一致性.

关键词:
盒子嵌入其他国家进行分类存在学

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科学领域:

  • 人工智能
  • 化学信息学
  • 生物信息学

背景情况:

  • 深度学习模型缺乏透明度, 阻碍了符号知识的提取.
  • 解释性AI对于理解复杂模型输出至关重要.
  • 多标签分类任务通常涉及固有的层次标签结构.

研究的目的:

  • 开发一种从深度学习模型中提取符号知识的方法.
  • 在模型输出中强制执行分类结构以提高可解释性.
  • 用几何嵌入来表示多标签数据集中的隐含逻辑关系.

主要方法:

  • 在矢量空间中使用盒形嵌入式的本体类.
  • 在培训过程中强制执行模型输出的分类结构.
  • 通过在ChEBI本体学中近似的子类关系来评估模型性能.

主要成果:

  • 该模型成功地捕获了标签之间的隐性等级关系.
  • 确保与底层的本体概念的一致性.
  • 在多标签分类任务中实现了最先进的性能.

结论:

  • 提出的方法可以在化学分类中提供可解释的输出.
  • 分子和类的几何表示有助于理解逻辑关系.
  • 在培训过程中没有明确的分类学,隐含的等级学会.