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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
Constructing a...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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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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相关实验视频

Updated: Jul 24, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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通过概率训练实现内在的对抗性强度.

Junhao Dong, Lingxiao Yang, Yuan Wang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |July 10, 2023
    PubMed
    概括

    逆向概率训练 (APT) 通过建模逆向分布来增强深度学习安全性,弥合自然和逆向示例之间的差距. 这种方法通过解决当前对抗训练方法中的优化偏差来提高对攻击的稳定性.

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 深度学习安全 深度学习安全

    背景情况:

    • 深度神经网络 (DNN) 容易受到对抗性干扰,从而构成安全风险.
    • 当前的对抗性训练方法优化了自然例子,忽略了对抗性领域对手,导致决策边界低于最佳并降低了稳定性.

    研究的目的:

    • 提出对抗概率训练 (APT) 以提高DNN的稳定性.
    • 通过建模潜在的对抗分布,弥合自然和对抗例子之间的分布差距.

    主要方法:

    • 在效率的特征水平上估计对抗分布参数.
    • 使用对抗性概率模型和原始对抗性示例进行脱分布对齐.
    • 实施一种新的重量机制,考虑到对抗力和领域不确定性.

    主要成果:

    • APT表现出对各种对抗性攻击的卓越性能.
    • 该方法在不同的数据集和场景中显示出有效性.
    • 拟议的方法减轻了优化偏差的负面影响.

    结论:

    • 对抗概率训练 (APT) 提供了一种更有效的策略,用于提高DNN中的对抗稳定性.

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  • 功能级估计和新型重权重机制有助于增强对复杂攻击的安全性.