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相关概念视频

Associative Learning01:27

Associative Learning

276
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...
276
Observational Learning01:12

Observational Learning

118
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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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Purposive Learning01:22

Purposive Learning

96
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...
96
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

399
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...
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相关实验视频

Updated: May 24, 2025

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

Published on: December 6, 2024

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AUCPro:以AUC为导向的可证明强度学习

Shilong Bao, Qianqian Xu, Zhiyong Yang

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    概括
    此摘要是机器生成的。

    本研究介绍了AUCPro,这是一个在深度神经网络 (DNN) 中可证明稳健性的新框架,它解决了不平衡的数据集. AUCPro针对ROC曲线下的区域 (AUC) 进行了优化,提高了现实世界安全关键应用中的模型可靠性.

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    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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    科学领域:

    • 机器学习 机器学习
    • 深度神经网络 深度神经网络
    • 竞争力强度 竞争力强度

    背景情况:

    • 对于深度神经网络 (DNN) 的传统可证明稳定性方法通常假设平衡的类分布.
    • 现实世界的应用程序,特别是安全敏感的系统,经常遇到不平衡的数据集,长尾分布.
    • 与准确性相比,ROC曲线下的面积 (AUC) 是评估不平衡数据集上的模型性能的一个更合适的指标.

    研究的目的:

    • 为长尾分布设计的第一个以AUC为导向的可证明稳定性学习框架 (AUCPro).
    • 从理论上分析拟议的AUCPro框架的认证安全区域和稳定性概括.
    • 在基于AUC的稳健性背景下,研究性能-稳健性权衡和过度风险.

    主要方法:

    • 开发了AUCPro,这是一个利用由同位素高斯噪声平滑的代理模型的框架.
    • 从面向AUC的学习角度优化了代理模型.
    • 获得了针对 $\ell _{2}$l2 对抗性攻击的认证安全区域的理论保证,并提出了强度概括的新型标准.

    主要成果:

    • 为AUCPro建立了一个经过认证的安全区域,确保该区域内不受敌对攻击.
    • 提出了一个新的理论框架来研究可证明的稳定性方法的稳定性概括,将其与AUC的对抗性风险联系起来.
    • 通过全面的实验证明了AUCPro的有效性,验证了其性能和通用化能力.

    结论:

    • 对于面临数据分布不平衡的深度神经网络,AUCPro提供了一个强大的解决方案.
    • 该研究引入了一种新的方法,用于理解和改进可证明的强度学习中的强度概括.
    • 这些发现对于在安全关键领域开发可靠的人工智能系统至关重要,这些领域具有长尾数据.