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相关概念视频

Associative Learning01:27

Associative Learning

461
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...
461
Parallel Processing01:20

Parallel Processing

186
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
186
Introduction to Learning01:18

Introduction to Learning

479
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
479
Purposive Learning01:22

Purposive Learning

156
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...
156
Observational Learning01:12

Observational Learning

225
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...
225
Machines: Problem Solving II01:30

Machines: Problem Solving II

336
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
336

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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针对任意图像的设备性能驱动的异质多方学习

Yuanqiao Zhang, Maoguo Gong, Yuan Gao

    IEEE transactions on neural networks and learning systems
    |June 13, 2023
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了一种多方学习 (MPL) 的新方法,以应对数据和模型异质性的挑战. 设备性能驱动的异质MPL (HMPL) 框架通过适应各种设备功能和数据分布来增强协作学习.

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    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 分布式系统 分布式系统

    背景情况:

    • 多方学习 (MPL) 促进了保护隐私的协作模式培训.
    • 越来越多的用户数量加剧了MPL中的数据和模型异质性.
    • 现有的MPL框架在不同的数据大小和设备计算能力方面扎.

    研究的目的:

    • 提出一种新的个人多方学习方法,HMPL,解决数据和模型异质性.
    • 开发适应性策略,统一来自具有任意数据大小的设备的特征图.
    • 根据单个设备的性能来实现定制的模型生成.

    主要方法:

    • 引入了一种异质的特征地图集成方法,用于各种特征地图的自适应统一.
    • 为性能驱动定制提出了一个分层模型生成和聚合策略.
    • 基于语义层对应的共享模型参数更新的实施聚合规则.

    主要成果:

    • 在多方学习中,HMPL框架有效地解决了数据和模型异质性的问题.
    • 在四个数据集上的实验结果表明,与最先进的方法相比,性能优越.
    • 提出的方法显示了与异质设备的协作学习的显著改进.

    结论:

    • 在异质环境中,HMPL为保护隐私的协作学习提供了强大的解决方案.
    • 该框架的自适应策略增强了模型性能和概括性.
    • 这项工作通过解决实际的异质性挑战,推动了多方学习领域的发展.