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相关概念视频

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...
895
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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 of...
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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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Cognitive Learning01:21

Cognitive 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.
Tolman introduced the idea that behavior is influenced by...
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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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相关实验视频

Updated: Jan 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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大型视觉语言模型 持续学习 与专家的动态混合

Yizhou Chen, Xihao Huang, Wei Zhang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |December 10, 2025
    PubMed
    概括

    本研究引入了视觉语言模型 (VLM) 的新持续学习框架. 它使VLM能够学习新任务而不忘记旧任务,提高动态数据集的性能.

    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:

    • 在动态环境中的视觉语言模型 (VLMs) 中,持续学习至关重要.
    • 由于任务数量增加,现有的方法在可扩展性和性能下降方面扎.
    • 灾难性遗忘仍然是VLM持续学习的重大挑战.

    研究的目的:

    • 为VLMs开发一种新的持续学习框架,解决可扩展性和灾难性遗忘问题.
    • 为了使VLM能够适应越来越多的开放式任务,同时保持历史知识.
    • 为了减少可调节参数,并改善模型复杂性和容量之间的权衡.

    主要方法:

    • 拟议的框架建立在预先训练的CLIP模型之上.
    • 它包含了一个动态的专家组合 (MoE) 层,以灵活地适应任务.
    • 使用弹性专家体重管理策略和自适应级的LoRA专家来缓解忘记和优化性能.

    主要成果:

    • 与现有方法相比,该方法显示可调节参数的显著减少.
    • 它在学习新任务方面始终优于最先进的方法.
    • 有效地保持了对历史任务的性能,克服了灾难性的遗忘.

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    结论:

    • 拟议的框架为VLMs的持续学习提供了有效的解决方案.
    • 它使VLM能够有效地处理动态的,开放式的任务,而不会损失知识.
    • 这种方法在现实世界,不断发展的应用场景中提升了VLM的功能.