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相关概念视频

Concepts and Prototypes01:24

Concepts and Prototypes

149
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,...
149
Stereotype Content Model02:16

Stereotype Content Model

14.7K
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...
14.7K
Classification of Systems-I01:26

Classification of Systems-I

188
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
188
Classification of Systems-II01:31

Classification of Systems-II

146
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
146
The Representativeness Heuristic02:13

The Representativeness Heuristic

15.8K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
15.8K
Aggregates Classification01:29

Aggregates Classification

326
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
326

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相关实验视频

Updated: Jul 6, 2025

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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关于基于部分原型的分类器的可解释性:以人为中心的分析.

Omid Davoodi1, Shayan Mohammadizadehsamakosh2, Majid Komeili3

  • 1School of Computer Science, Carleton University, Ottawa, ON, Canada. omid.davoudi@carleton.ca.

Scientific reports
|December 28, 2023
PubMed
概括

我们开发了一个框架来评估人类如何理解部分原型网络,一种可解释的AI. 我们的综合实验证实了它在评估模型解释性方面的有效性.

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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相关实验视频

Last Updated: Jul 6, 2025

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 人与计算机的交互

背景情况:

  • 部分原型网络为黑子图像分类器提供了一个可解释的替代方案.
  • 这些模型以人为中心的可解释性仍然未被充分探索.
  • 之前的研究往往受到错误的实验设计的影响,限制了可靠性.

研究的目的:

  • 提出一个强大的框架来评估人类感知部分原型模型的可解释性.
  • 解决先前工作中的实验设计和任务表示的局限性.
  • 为评估模型可解释性提供可靠和有效的指标.

主要方法:

  • 开发了一个新的框架,有三个可操作的指标和实验程序.
  • 使用亚马逊机械土耳其人进行了广泛的人体实验.
  • 专注于以人为中心的评估部分原型网络的可解释性.

主要成果:

  • 拟议的框架有效地评估了各种部分原型模型的可解释性.
  • 实验给出了对这些模型的可解释性质的可靠见解.
  • 证明了框架能够克服以前的方法问题.

结论:

  • 开发的框架提供了一种可靠的方法来评估部分原型网络的人类可解读性.
  • 这项工作代表了在统一的结构中对部分原型模型可解释性的全面评估.
  • 这些发现为更可靠,更易于理解的AI系统铺平了道路.