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相关概念视频

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.
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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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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.
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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
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Anchoring junctions are multiprotein complexes that help cells connect to other cells and the extracellular matrix. Anchoring junctions are present on the lateral and basal surfaces of cells, providing strong and flexible connections. Focal adhesions are often formed due to cell interactions with the ECM substrata, which initiate signal transduction via kinase cascades and other mechanisms. Together, they provide stability and tissue integrity. There are three types of anchoring junctions:...
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FedDNA:使用动态节点对齐进行联合学习.

Shuwen Wang1, Xingquan Zhu1

  • 1Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida, United States of America.

PloS one
|July 3, 2023
PubMed
概括
此摘要是机器生成的。

联合学习 (FL) 可以通过动态节点对齐算法FedDNA进行改进. 通过在分布式站点中智能匹配节点,FedDNA优化了模型训练,优于像FedAvg.这样的静态方法.

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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
  • 计算机科学 计算机科学

背景情况:

  • 联邦学习 (FL) 提供保护隐私的模型培训.
  • 当前的FL方法经常使用静态节点对齐,这可能是次优的.
  • 神经网络中各个节点的角色是复杂和动态的.

研究的目的:

  • 介绍FedDNA,一种使用动态节点对齐的新型联合学习算法.
  • 通过改善分布式站点之间的节点匹配来提高联合学习的性能.
  • 解决现有的联合学习方法中静态节点匹配的局限性.

主要方法:

  • 将神经网络节点的重量表示为向量.
  • 使用距离函数来识别分布式站点中的类似节点.
  • 使用最小跨树方法来实现高效和全面的节点匹配.

主要成果:

  • 为了改进联合学习,FedDNA动态对齐节点.
  • 该算法有效地在不同站点找到最佳节点匹配.
  • 实验结果显示,FedDNA超越了传统方法,如FedAvg.

结论:

  • 动态节点对齐是推动联合学习的一个有希望的策略.
  • 对于节点匹配,FedDNA提供了一个计算高效和有效的解决方案.
  • 拟议的方法在联合学习环境中增强了模型性能和隐私.