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相关概念视频

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

468
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...
468
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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Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
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.
Synarthrosis
An...
3.9K
Structural Classification of Joints01:20

Structural Classification of Joints

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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.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.2K
Introduction to Learning01:18

Introduction to Learning

342
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...
342
Observational Learning01:12

Observational Learning

149
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...
149

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Updated: Jun 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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持续解的联合空间学习,用于域泛化.

Zizhou Wang, Yan Wang, Yangqin Feng

    IEEE transactions on neural networks and learning systems
    |September 20, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了用于域泛化 (DG) 的连续解合空间学习 (CJSL). CJSL有效地利用了域不变和域特定的信息,超过了19种最先进的方法.

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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
    • 计算机视觉 计算机视觉

    背景情况:

    • 域泛化 (DG) 旨在开发在新域未见数据上表现良好的模型.
    • 现有的 DG 方法往往侧重于域不变的特征,可能忽视有价值的域特定语义信息.

    研究的目的:

    • 提出一个新的 DG 方法,即连续解的联合空间学习 (CJSL),它利用了域不变和域特定的信息.
    • 通过模拟测试样本的特定领域表示方式来提高GD模型的稳定性和有效性.

    主要方法:

    • 通过代的特征解,CJSL 通过代的特征解来制定和学习一个连续的联合空间 (CJS),用于域特定的表示.
    • 在推断过程中,CJSL通过使用蒙特卡洛方法从学习的CJS抽取样本来模拟测试样本的域特定表示.
    • 这种方法可以使用多个域特定分类器进行可靠的预测.

    主要成果:

    • 与19种最先进的 (SOTA) 方法相比,CJSL表现优越.
    • 提出的方法在七个基准数据集中取得了显著的改进.
    • 经验结果验证了利用域不变和模拟域特定信息的有效性.

    结论:

    • 通过整合域不变和域特定特征学习,CJSL为域泛化提供了一种新有效的方法.
    • 该方法模拟域特定表示的能力增强了对未见域的模型概括性.
    • 拟议的技术代表了域泛化领域的重大进展.