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相关概念视频

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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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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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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Randomized Experiments01:13

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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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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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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相关实验视频

Updated: Jul 24, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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一个基于元学习的优化黑盒对抗模拟器攻击.

Zhiyu Chen1, Jianyu Ding2, Fei Wu2

  • 1School of Internet of Things, Nanjing University of Posts and Telecommunication, Nanjing 210023, China.

Entropy (Basel, Switzerland)
|July 8, 2023
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概括
此摘要是机器生成的。

研究人员使用新的模拟器攻击+增强了黑子对抗性攻击. 这种方法通过更好地利用深度神经网络中的特征信息来提高查询效率,使攻击更有效.

关键词:
敌对攻击是对抗性的攻击.黑子攻击攻击黑子攻击梯度优化优化 梯度优化知识的蒸知识的蒸.这就是meta-learning.

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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
  • 网络安全 网络安全

背景情况:

  • 深度神经网络 (DNN) 存在安全漏洞,使其容易受到对抗性攻击.
  • 由于DNN固有的不透明性,黑子对抗性攻击尤其现实.
  • 现有的黑子攻击方法往往无法充分利用可用的查询信息.

研究的目的:

  • 在对抗性攻击中验证从元学习模拟器模型中特征层信息的实用性.
  • 引入一个优化的黑子攻击方法,模拟器攻击+,以提高查询效率.
  • 为了提高在DNN中对抗性示例生成的性能.

主要方法:

  • 该研究验证了从meta-learned模拟器模型中使用特征层信息的使用.
  • 拟议的模拟器攻击+包括一个功能注意力提升模块.
  • 实现了线性自适应模拟器预测间隔机制和针对目标攻击的无监督集群模块.

主要成果:

  • 模拟器攻击+显著减少了成功对抗攻击所需的查询数量.
  • 提出的优化提升了生成对抗性示例的效率.
  • 对CIFAR-10和CIFAR-100数据集的实验结果表明,查询效率提高,同时保持攻击成功.

结论:

  • 来自模拟器模型的特征层信息对于有效的黑子对抗性攻击至关重要.
  • 模拟器攻击+在优化黑子攻击的查询效率方面取得了重大进展.
  • 开发的方法为评估DNN安全漏洞提供了更实用和更有效的方法.