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相关概念视频

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.
Classical conditioning, also known...
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Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

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The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
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Inductive Reasoning00:59

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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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Prediction Intervals01:03

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Multi-input and Multi-variable systems01:22

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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.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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SIA:一个可持续的推断攻击框架在分割学习.

Fangchao Yu1, Lina Wang1, Bo Zeng1

  • 1Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, 430072, China.

Neural networks : the official journal of the International Neural Network Society
|December 23, 2023
PubMed
概括
此摘要是机器生成的。

这项研究增强了分割学习攻击,以高效地重建客户端数据,即使是垂直分区的数据. 这种先进的攻击逃避检测,对分割学习应用程序构成重大隐私风险.

关键词:
功能空间 功能空间推断攻击是一种推断攻击.一个影子模型.分拆学习是指分开学习.

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科学领域:

  • 计算机科学 计算机科学
  • 机器学习 机器学习
  • 网络安全 网络安全

背景情况:

  • 分分学习是一种分布式的框架,用于资源有限的联合培训.
  • 现有的功能空间劫持攻击通过重建客户端数据构成隐私风险.
  • 当前的防御可能无法充分保护对复杂的攻击.

研究的目的:

  • 开发一个增强的攻击框架,以便在分割学习中高效地重建数据.
  • 将数据重建攻击扩展到垂直分区的数据场景.
  • 为了创建一个逃避最先进的检测机制的攻击.

主要方法:

  • 开发了一个增强的功能空间劫持攻击.
  • 在分割学习中适应了对垂直分区数据的攻击.
  • 引入了三种攻击训练模式以提高灵活性.
  • 评估了跨数据集和防御的攻击有效性,隐形性和普遍性.

主要成果:

  • 增强攻击实现了高质量的数据重建,对主要任务的影响最小.
  • 攻击成功地绕过了当前最先进的检测机制.
  • 攻击是有效的,并且可以在各种数据集和场景中进行概括.
  • 攻击成功地扩展到具有挑战性的垂直分区数据设置.

结论:

  • 分解学习面临着来自先进重建攻击的重大,低估的隐私风险.
  • 开发的攻击框架展示了有效性,隐形性和适应性.
  • 突出了在分割学习应用程序中迫切需要强有力的安全措施.