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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.
Classical conditioning, also known...
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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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Introduction to Learning01:18

Introduction to Learning

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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...
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Purposive Learning01:22

Purposive Learning

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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...
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Language Development01:22

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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以语言为灵感的关系转移为短暂的课堂增量学习.

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    这项研究引入了一种新的语言灵感关系转移 (LRT) 方法,用于少量射击类增量学习 (FSCIL). 通过结合视觉和文本数据,LRT增强了对象识别,优于现有的模型.

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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:

    • 少数射击类增量学习 (FSCIL) 旨在使系统能够从有限的例子中学习新类,同时保留现有知识.
    • 由于依赖视觉编码器调整,当前的FSCIL方法经常在基础知识和增量知识之间进行权衡.
    • 人类学习有效地结合了语言描述来识别新概念,这是许多人工智能系统缺乏的能力.

    研究的目的:

    • 提出一种新的语言灵感关系转移 (LRT) 范式,用于少量射击类增量学习 (FSCIL).
    • 利用视觉线索和文本描述来提高对象的理解和分类在开放世界的设置.
    • 克服现有方法的局限性,解决增量学习中的知识权衡和领域差距.

    主要方法:

    • 开发了一个两步的LRT范式,整合视觉和语言信息.
    • 引入了一个图形关系转换模块,以将预先训练的文本知识转移到视觉领域.
    • 实现了一个文本视觉原型融合模块,用于结合视觉和语言嵌入.
    • 利用上下文提示学习来快速调整域,以及想象的对比学习来在调整过程中处理有限的文本数据.

    主要成果:

    • 与最先进的模型相比,拟议的LRT范式表现出优越的性能.
    • 在miniImageNet FSCIL基准指标上取得了超过13%的改进.
    • 在CIFAR-100 FSCIL基准指标上取得了7%以上的改善.

    结论:

    • 通过将视觉和语言模式协同使用,LRT范式有效地增强了Few-Shot类增量学习.
    • 建议的域调整和文本图像转移的方法成功地减轻了增量学习中的挑战.
    • 低速技术为开发能够进行终身学习的更强大,更具适应性的人工智能系统提供了一个有前途的方向.