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相关概念视频

Observational Learning01:12

Observational Learning

158
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Gradient and Del Operator01:14

Gradient and Del Operator

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In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Second Order systems I01:20

Second Order systems I

143
A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
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Differential Leveling01:12

Differential Leveling

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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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Second Order systems II01:18

Second Order systems II

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In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
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相关实验视频

Updated: Jun 19, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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零级梯度跟踪用于分散的学习,保证隐私.

Zhongyuan Zhao1, Lunchao Xia2, Luyao Jiang3

  • 1Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing, 210044, China; Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing, 210044, China; College of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China.

ISA transactions
|July 24, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了一种新的隐私保护优化算法,用于未知梯度的去中心化系统. 不同隐私分散的零级梯度跟踪 (DP-DZOGT) 算法确保了数据安全,同时实现了高效的优化.

关键词:
分散式的学习学习是分散式的不同的隐私差异性隐私.梯度跟踪跟踪 梯度跟踪一个零级梯度估计器.

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相关实验视频

Last Updated: Jun 19, 2025

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科学领域:

  • 优化理论 优化理论
  • 分布式系统 分布式系统
  • 网络安全 网络安全

背景情况:

  • 分散系统经常面临未知梯度信息的挑战,阻碍了优化.
  • 在分布式机器学习和智能电网中,保护个人代理隐私至关重要.
  • 现有的方法可能无法充分平衡隐私保护与优化效率.

研究的目的:

  • 提出一种新的算法,用于未知梯度的去中心化优化.
  • 整合不同的隐私机制来保护代理数据.
  • 确保拟议的算法在实际应用中的融合和有效性.

主要方法:

  • 开发一个差异隐私去中心化零阶梯度跟踪 (DP-DZOGT) 算法.
  • 构建一个一点零级梯度估计器 (OPZOGE) 用于梯度估计.
  • 将随机噪声引入到代理状态和梯度中,以增强隐私.

主要成果:

  • 该DP-DZOGT算法保证在固定步骤大小下的线性收.
  • 提出的方法有效地估计了仅使用函数值的梯度.
  • 在智能电网和分散的联合学习场景中证明了应用和验证.

结论:

  • DP-DZOGT算法为分散系统中的私有优化提供了一个强大的解决方案.
  • 不同隐私的整合有效地保护了敏感的代理信息.
  • 该算法在智能电网和分散的联合学习中增强安全性和性能方面表现有前途.