Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.9K
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...
1.9K
Observational Learning01:12

Observational Learning

317
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...
317
Neural Regulation01:37

Neural Regulation

40.2K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
40.2K
Reinforcement01:23

Reinforcement

347
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
347
Survival Tree01:19

Survival Tree

164
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...
164
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

2.4K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
2.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Screening high-enzyme-producing strains from fermented rice slurry to enhance amylose content and remodel starch structure in rice noodles.

Food chemistry: X·2025
Same author

Modulating Macrophage Polarization for Severe Acute Pancreatitis Therapy via Cisplatin-like Prussian Blue Nanozymes.

Theranostics·2025
Same author

Stabilizing Lattice Oxygen via Interfacial B-O Complexing for a 4.6 V LiCoO<sub>2</sub> Cathode.

ACS nano·2025
Same author

Welfare Policies, Joint Pain Prevalence and Educational Gaps in 50 U.S. States from 2011 to 2021: A Fixed Effects Analysis.

medRxiv : the preprint server for health sciences·2025
Same author

Welfare policies, joint pain prevalence and educational gaps in 50 U.S. states from 2011 to 2021: A fixed effects analysis.

The journal of pain·2025
Same author

Training of physical neural networks.

Nature·2025

相关实验视频

Updated: Sep 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

644

应用强化学习来保护深度神经网络免受软错误的影响

Peng Su1, Yuhang Li1, Zhonghai Lu2

  • 1Department of Engineering Design, KTH Royal Institute of Technology, 10044 Stockholm, Sweden.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
概括

这项研究引入了一种新的强化学习方法,通过识别和掩盖易受攻击的位来保护深度神经网络免受软错误的影响,从而显著提高了系统的稳定性和安全性.

科学领域:

  • 人工智能的人工智能
  • 计算机科学 计算机科学
  • 电气工程 电气工程

背景情况:

  • 深度神经网络 (DNN) 对基于传感器的系统至关重要,但容易发生软错误,威胁到系统安全.
  • 传统的容错方法在复杂的DNN架构中面临着可扩展性挑战.
  • 确保DNN对错误的稳定性对于可靠的AI应用程序至关重要.

研究的目的:

  • 开发一种有效和可扩展的方法来保护DNN免受软错误.
  • 使用一种新的方法,在DNN中识别和减轻脆弱位.
  • 为了提高人工智能驱动的传感器系统的安全性和可靠性.

主要方法:

  • 开发了一个基于强化学习 (RL) 的代理来识别DNN中的脆弱位.
  • 使用故障注入模拟来分析层层的网络弹性.
  • 转移学习被用于有效合成和部署比特面具.

主要成果:

  • 提出的基于RL的方法显示,与基线技术相比,显著提高了性能 (10-15%).
  • 易受攻击的位被动态和高效地保护,提高了网络的稳定性.
  • 这种方法在保护选定的神经网络免受软错误时被证明是有效的.
关键词:
错误注入的错误注入方式强化学习是一种强化学习.软错误保护保护软错误保护

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

相关实验视频

Last Updated: Sep 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

644
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

结论:

  • 与传统方法相比,基于RL的方法为保护DNN免受软错误提供了优越的解决方案.
  • 这种技术通过减轻软错误影响来提高人工智能系统的安全性和可靠性.
  • 该方法提供了一种可扩展和有效的方式来提高DNN的弹性.