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相关概念视频

Survival Tree01:19

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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.
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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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End Point Prediction: Gran Plot01:07

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Heuristics01:21

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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Updated: Jun 14, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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对无监督图形表示学习的后门攻击.

Bingdao Feng1, Di Jin1, Xiaobao Wang1

  • 1College of Intelligence and Computing, Tianjin University, Tianjin, China.

Neural networks : the official journal of the International Neural Network Society
|September 7, 2024
PubMed
概括
此摘要是机器生成的。

无监督的图形学习容易受到后门攻击. 我们介绍了GRBA,这是一种新的方法,可以在没有事先任务知识的情况下有效地对未标记的图形数据进行这些攻击.

关键词:
后门攻击后门攻击触发器 触发器 触发器没有监督的图形学习.

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 图形分析分析 图形分析

背景情况:

  • 无监督的图形学习方法,通常使用相互信息最大化,生成节点和图形表示.
  • 这些技术越来越受欢迎,但缺乏对数据中毒攻击的强有力的防御.
  • 现有的后门攻击通常是为监督的设置而设计的,不适合未标记的图形数据.

研究的目的:

  • 调查无监督图形学习对后门攻击的脆弱性.
  • 提出一种新的攻击方法,GRBA,专门用于无监督图形学习设置.
  • 在各种下游任务中展示拟议攻击的有效性和多功能性.

主要方法:

  • 引入了GRBA (梯度式表示后门攻击),这是一个一级的梯度式攻击.
  • 设计的GRBA直接针对节点和图表表示,而不需要下游任务信息.
  • 应用GRBA来毒害未标记的图形数据,通过引入节点特征和图形结构中的触发器.

主要成果:

  • 证明无监督的图形学习模型容易受到后门攻击.
  • 展示了GRBA在破坏学习表征和影响下游任务方面的有效性.
  • 验证了GRBA对节点和图层任务的最先进的无监督学习模型的性能.

结论:

  • 无监督图形学习方法为后门对手提供了一个新的攻击面.
  • 在这个领域,GRBA为后门攻击提供了一种先进而有效的方法.
  • 攻击与下游任务具体情况的独立性突出了其广泛的适用性和潜在风险.