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相关概念视频

Observational Learning01:12

Observational Learning

97
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...
97
Associative Learning01:27

Associative Learning

236
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...
236
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

85
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
85
State Space Representation01:27

State Space Representation

145
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...
145
Cognitive Learning01:21

Cognitive Learning

93
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...
93
Purposive Learning01:22

Purposive Learning

86
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...
86

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USTEP:在统一的视图下进行空间时间预测学习.

Cheng Tan, Jue Wang, Zhangyang Gao

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    此摘要是机器生成的。

    本研究介绍了USTEP,这是一个新的空间时间预测学习框架. USTEP统一了现有的方法,在各种应用中显著提高了性能.

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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
    • 机器学习 机器学习

    背景情况:

    • 时空预测学习对于自我监督学习和各种应用至关重要.
    • 现有的时间建模方法 (基于循环和无循环) 在效率和依赖性处理方面存在局限性.

    研究的目的:

    • 重新审视和统一空间-时间预测学习中占主导地位的时间建模方法.
    • 引入一个创新的框架,USTEP,集成微观和宏观时间尺度.

    主要方法:

    • 一个统一的视角,以循环为基础的和无循环的时间建模.
    • 开发USTEP (统一空间-时间预测学习) 框架.
    • 在USTEP框架内整合微时间和宏时间尺度.

    主要成果:

    • 与现有的时间建模方法相比,USTEP显示出了显著的改进.
    • 该框架在广泛的时空预测学习任务中实现了强大的性能.
    • USTEP为时空应用建立了一个新的基准.

    结论:

    • USTEP有效地调和了基于循环和无循环方法的优势.
    • 拟议的框架为时空预测学习提供了强大而高效的解决方案.
    • USTEP为各种时空应用提供了一种多功能工具.