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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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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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相关实验视频

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在深度神经网络中识别新兴概念的方法.

Tim Räz1

  • 1University of Bern, Institute of Philosophy, Länggassstrasse 49a, 3012 Bern, Switzerland.

Patterns (New York, N.Y.)
|July 6, 2023
PubMed
概括

深度神经网络 (DNN) 学习概念,但由于基于实例的概念定义,目前的检测方法不可靠. 结合方法和使用合成数据可以提高理解DNN概念形成的可靠性.

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 认知科学 认知科学

背景情况:

  • 深度神经网络 (DNN) 拥有内部表征,可以学习概念.
  • 在DNN中检测概念的现有方法包括网络剖析,特征可视化和概念激活矢量 (TCAV).

研究的目的:

  • 批判性地评估当前用于在DNN中检测概念的方法.
  • 强调基于实例的概念规范的局限性,并提出解决方案.
  • 探索DNN中概念空间的形成及其相关的权衡.

主要方法:

  • 讨论和批判性分析现有的概念检测技术 (网络剖析,特征可视化,TCAV).
  • 探索实例选择在概念定义中的作用.
  • 考虑结合方法并使用合成数据集以提高可靠性.
  • 通过准确性-压缩权衡来塑造概念空间的分析.

主要成果:

  • 目前的方法为DNN学习非碎的概念关系提供了证据.
  • 基于实例的概念规范导致不确定性和不可靠性.
  • 结合方法和使用合成数据可以部分减轻不可靠性.
  • 概念空间对于理解概念形成至关重要,但缺乏专门的研究方法.
关键词:
在 TCAV TCAV 中.这些概念是概念的概念.深度神经网络是一个神经网络.功能可视化 功能可视化图像的分类图像的分类.内部代表的内部代表.可以解释的解释性.网络剖析 网络解剖

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结论:

  • 虽然DNN学习概念,但当前的检测方法具有固有的局限性.
  • 提高概念检测可靠性需要解决实例不确定性.
  • 需要进一步的研究来开发可靠的方法来研究DNN中的概念空间.