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相关概念视频

Survival Tree01:19

Survival Tree

60
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
Constructing a...
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Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

576
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
576
Variability: Analysis01:11

Variability: Analysis

126
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
126
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

364
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
364
Stability01:28

Stability

91
The time response of a linear time-invariant (LTI) system can be divided into transient and steady-state responses. The transient response represents the system's initial reaction to a change in input and diminishes to zero over time. In contrast, the steady-state response is the behavior that persists after the transient effects have faded.
The stability of an LTI system is determined by the roots of its characteristic equation, known as poles. A system is stable if it produces a bounded...
91
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
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相关实验视频

Updated: Jun 5, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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对抗性培训的数据依赖稳定性分析.

Yihan Wang1, Shuang Liu1, Xiao-Shan Gao1

  • 1Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 101408, China.

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

这项研究为深度学习中的对抗性训练引入了新的概括界限,包括数据分布. 这些边界提高了对强有力的概括和分布变化的影响的理解.

关键词:
对抗性的训练是对抗性的训练.数据中毒攻击是数据中毒攻击.一般化有限的概括.在平均稳定性分析上进行稳定性分析.随机梯度下降 随机梯度下降

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科学领域:

  • 深度学习 (Deep Learning) 是一种深度学习.
  • 机器学习理论机器学习理论
  • 人工智能中的稳定性

背景情况:

  • 稳定性分析对于深度学习的一般化至关重要.
  • 敌对训练是对抗攻击的关键防御.
  • 现有的概括界限缺乏数据分布信息.

研究的目的:

  • 为包括数据分布在内的对抗性训练提供概括界限.
  • 分析数据分布和对抗预算对概括差距的影响.
  • 提高对深度学习中强大的概括的理解.

主要方法:

  • 使用平均稳定性和高阶近似的利普希茨条件.
  • 导出对凸和非凸损失的概括界限.
  • 分析分配转移和对抗预算的影响.

主要成果:

  • 开发了新的概括界限,包括用于对抗训练的数据分布.
  • 边界与现有的基于稳定性的统一边界相似或更高.
  • 证明了分布从数据中毒转移到强大的概括的影响.

结论:

  • 拟议的概括界限为对抗训练的强度提供了更深入的见解.
  • 数据分布在对抗性环境中显著影响了概括差距.
  • 这些发现对于开发针对各种攻击的更有弹性的深度学习模型至关重要.