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相关概念视频

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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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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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
228

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通过潜结构感知序列自动编码器演变域泛化.

Tiexin Qin, Shiqi Wang, Haoliang Li

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    概括
    此摘要是机器生成的。

    本研究介绍了对非静止环境的演变域泛化,并建议MMD-LSAE处理连续域漂移以提高机器学习模型性能.

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    科学领域:

    • 机器学习 机器学习
    • 人工智能的人工智能
    • 计算机视觉 计算机视觉

    背景情况:

    • 域泛化 (DG) 传统上针对静止环境中的分布外 (OOD) 数据.
    • 像自动驾驶这样的现实应用面临着不断演变的领域漂移,这对现有的 DG 方法构成挑战.

    研究的目的:

    • 解决目前在非静止环境中的总局方法的局限性.
    • 引入和验证一个新的框架,用于不断发展的域泛化.

    主要方法:

    • 提出MMD-LSAE,这是一个旨在捕捉非静止领域不断变化的模式的框架.
    • 描述OOD数据转移到共变量和概念转移,使用深度自动编码器推断它们的动态.
    • 通过最大平均差异 (MMD) 优化潜伏空间分布,以对齐前后.

    主要成果:

    • MMD-LSAE有效地捕捉了不断变化的域模式,以提高概括性.
    • 该框架通过隐式地促进相互信息最大化来证明优越的代表性学习.
    • 在合成和现实数据上的实验结果证实了该方法的良好表现.

    结论:

    • MMD-LSAE提供了一个强大的解决方案,用于在非静止设置中演变域泛化.
    • 该方法提高了模型适应持续域漂移的适应性.
    • 这项工作推进了动态现实世界的应用领域的域概括领域.