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相关概念视频

Classification of Systems-I01:26

Classification of Systems-I

219
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
219
Binomial Probability Distribution01:15

Binomial Probability Distribution

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
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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
Constructing a...
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Linear time-invariant Systems01:23

Linear time-invariant Systems

297
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
297
Classification of Systems-II01:31

Classification of Systems-II

179
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
179
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

106
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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在二进制线性分类中对抗性训练的非对称行为.

Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis

    IEEE transactions on neural networks and learning systems
    |July 18, 2023
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    概括

    敌对训练提高了对攻击的分类稳定性. 这项研究准确地描述了对抗性训练的特征.

    科学领域:

    • 机器学习 机器学习
    • 计算机视觉 计算机视觉
    • 人工智能中的稳定性

    背景情况:

    • 经验风险最小化 (ERM) 的对抗性培训是对抗性攻击的首要防御.
    • 了解对抗训练的概括性质仍然是一个挑战.
    • 本研究侧重于在高维设置中的二进制线性分类.

    研究的目的:

    • 准确地描述二进制线性分类中的对抗训练的稳定性.
    • 在对抗性扰动下分析概括性质.
    • 调查对抗训练的基本局限性.

    主要方法:

    • 在高维模式下进行分析,模型尺寸与训练集尺寸相匹配.
    • 对标准和对抗性测试错误的精确非对称错误公式的推导.
    • 在歧视性和生成性模型中考虑一般的p-规范局限性扰动.

    主要成果:

    • 在对抗性扰动下测试错误的精确异交式公式.
    • 解释过度参数化,数据模型和攻击预算如何影响错误.
    • 与强大的贝叶斯估计器进行比较,以了解基本限制.

    结论:

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    • 在二进制线性分类中提供对抗训练稳定性的精确表征.
    • 提供了关键因素对对抗性和标准错误的影响的见解.
    • 建立了一个研究对抗训练防御的理论限制的框架.