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相关概念视频

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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Role of Shaping in Operant Conditioning01:19

Role of Shaping in Operant Conditioning

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Shaping is a technique used in operant conditioning to train complex behaviors by rewarding successive approximations toward the target behavior. This method is necessary because organisms are unlikely to perform complex behaviors spontaneously. Instead, shaping breaks down the desired behavior into small, manageable steps.
The steps involved in shaping begin with reinforcing any response that resembles the desired behavior. For example, parents might praise a child for picking up one toy. As...
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Storage01:23

Storage

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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Purposive Learning01:22

Purposive Learning

153
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
153
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
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相关实验视频

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Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze
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Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze

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形状电荷学习架构用于人机团队.

Boris Galitsky1, Dmitry Ilvovsky2, Saveli Goldberg3

  • 1Knowledge-Trail, San Jose, CA 93635, USA.

Entropy (Basel, Switzerland)
|June 28, 2023
PubMed
概括

这项研究为kNN架构引入了一种新的超学习/深度神经网络 (DNN). 这种方法提高了人机团队的可解释性和稳定性,克服了DNN的局限性.

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 人与计算机的交互

背景情况:

  • 深度学习 (DNN) 和变压器显示了人机团队的局限性.
  • 目前的DNN缺乏可解释性,明确的概括洞察力,以及与推理技术的强有力的集成.
  • DNN 存在对敌对攻击的脆弱性,阻碍了它们在协作团队中的使用.

研究的目的:

  • 提出一个新的Meta-learning/DNN → kNN架构.
  • 在支持人机团队方面克服独立DNN的局限性.
  • 为了提高可解释性,可解释性和对抗对抗攻击的稳定性.

主要方法:

  • 在对象层面上将深度学习与可解释的kNN (k-Nearest Neighbors) 集成.
  • 基于演推理的元级控制学习过程的实施.
  • 验证和纠正预测以提高可解释性.

主要成果:

  • 与传统的DNN相比,拟议的架构提供了更大的解释性.
  • 改进了与各种推理技术集成的机制.
  • 在团队设置中加强对抗对手攻击的防御.
关键词:
深度学习和近邻学习.为人类机器团队提供机器学习支持.最大的产生产量.结构的生产结构.

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结论:

  • 超学习/DNN → kNN架构为人机团队提供了显著的进步.
  • 这种方法解决了DNN的关键局限性,促进了更可靠和可解释的协作.
  • 从结构和最大产生的角度分析建筑结构.