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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

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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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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
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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.
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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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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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通过自主节奏的对抗训练来提高对抗强度.

Lirong He1, Qingzhong Ai2, Xincheng Yang2

  • 1School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, China; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.

Neural networks : the official journal of the International Neural Network Society
|September 20, 2023
PubMed
概括

自律对抗训练 (SPAT) 通过逐步引入复杂的对抗示例来提高深度神经网络的稳定性. 这种方法提高了性能,并减轻了对抗训练中的过度装配和灾难性遗忘.

关键词:
敌对的强度 敌对的强度对抗性的训练是对抗性的训练.自己节奏的学习学习.

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科学领域:

  • 深度学习 (Deep Learning) 是一种深度学习.
  • 机器学习安全 机器学习安全
  • 计算机视觉 计算机视觉

背景情况:

  • 对抗性训练对于深度神经网络 (DNN) 的对抗性强度至关重要.
  • 像课程学习这样的现有方法面临着诸如缺乏定量攻击标准和灾难性遗忘等挑战.
  • 这些局限性阻碍了对抗训练的最佳表现和概括.

研究的目的:

  • 引入自动步调对抗训练 (SPAT),以提高DNN对抗的稳定性.
  • 解决对抗训练中的过度配合和灾难性遗忘问题.
  • 为攻击强度提供定量标准,并在不同类别之间平衡对立的例子.

主要方法:

  • SPAT使用来自整个数据集的对抗性示例构建学习过程.
  • 该模型最初以"简单"的对抗性示例进行训练,逐渐纳入"复杂"的示例.
  • 对抗性示例的难度在每个类中进行本地评估,以确保类间的平衡.

主要成果:

  • SPAT证明了对标准基准的各种攻击的有效性.
  • 在CIFAR100上,SPAT在针对PGD10攻击的强大精度上实现了1.7%的提升.
  • SPAT导致对抗重量扰动 (AWP) 的自然精度增加了3.9%.

结论:

  • SPAT提供了一种新且有效的方法来增强DNN中的对抗性稳定性.
  • 自主学习模式成功地减轻了过度适应和灾难性遗忘.
  • 可以将SPAT与其他先进的方法集成在一起,以进一步提高对手的防御能力.