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

Attribution Theory00:56

Attribution Theory

12.9K
Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
12.9K
Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

380
Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
380
Fundamental Attribution Error01:14

Fundamental Attribution Error

12.8K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
12.8K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.0K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.0K
Neural Circuits01:25

Neural Circuits

1.1K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.1K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

305
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
305

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

Symptom Clusters and Quality of Life in Cervical Cancer Patients During the Perioperative Period: A Longitudinal Study.

Patient preference and adherence·2026
Same author

Perioperative Symptom Trajectories and a Risk Prediction Model for Cervical Cancer: A Prospective Longitudinal Study.

International journal of women's health·2026
Same author

Lamprey 3D single-cell transcriptomics reveals ancestral and specialized features of the vertebrate brain.

Science (New York, N.Y.)·2026
Same author

p53 overexpression counteracts the pro-survival effect of Bcl-2 by restoring MAMs function in cutaneous squamous cell carcinoma.

Cell division·2026
Same author

Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.

Diagnostics (Basel, Switzerland)·2026
Same author

Herpes simplex virus 1 UL2 protein inhibits RIG-I-like receptor pathway-induced IFN-β activity by disrupting IRF3 activation.

International journal of medical microbiology : IJMM·2026
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

New results on prescribed-time synchronization of complex networks via intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Variance-constrained multi-view ensemble broad network for imbalanced data.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jun 17, 2025

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

2.4K

一种基于归因图的可解释方法,用于CNN.

Xiangwei Zheng1, Lifeng Zhang1, Chunyan Xu2

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, Shandong, China; State Key Laboratory of High-end Server & Storage Technology, Jinan, 250300, Shandong, China.

Neural networks : the official journal of the International Neural Network Society
|August 11, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于归因图的可解释方法,用于卷积神经网络 (CNN). 该方法通过分析网络结构和内核的重要性来增强CNN的解释性,有助于关键应用.

关键词:
归因图表 归因图表全国CNN是什么意思可以解释的CNN CNN.核心的重要性 核心重要性

更多相关视频

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.7K

相关实验视频

Last Updated: Jun 17, 2025

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

2.4K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.7K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 卷积神经网络 (CNN) 在与图像相关的任务中表现出色,但缺乏透明度,阻碍了医疗诊断和自动驾驶等关键领域的应用.
  • 现有的CNN的可解释性方法主要集中在基于特征的分析上,对网络的整体结构和参数角色的关注有限.
  • 了解CNN的内部运作对于在高风险决策过程中建立信任和可靠性至关重要.

研究的目的:

  • 提出一种新的方法,即基于归因图的CNN可解释方法 (AGIC),以提高卷积神经网络的可解释性.
  • 将CNN的整体结构建模为归因图 (At-GCs) 以实现全球和本地解释性.
  • 在CNN中直观地理解内部参数和卷积内核的作用.

主要方法:

  • 使用运行时参数和特征图构建了归属图 (At-GCs),将卷积内核表示为节点,SHAP值表示为边缘.
  • 在生成的At-GC上预先训练了一种使用Deep Graph Infomax (DGI) 的异质图形编码器.
  • 使用预训练的编码器完成了两个任务:分类At-GC以显示类别依赖的拓特征,并使用分数聚合 (SA) 网络来评估内核重要性.

主要成果:

  • 归因图的拓特征显示出对图像样本类别的依赖性,表明不同类别的不同内核激活模式.
  • 评分聚合网络成功地确定了用于特征提取的关键内核,并突出显示了可以在不降低性能的情况下修剪的内核.
  • AGIC 方法提供了对 CNN 结构和参数意义的更全面的理解.

结论:

  • 拟议的AGIC方法通过分析其基于图形的结构和内核的重要性,有效地提高了CNN的解释性.
  • 这些发现表明,CNN表现出特定类别的结构模式,为其决策过程提供了洞察力.
  • AGIC 便于识别基本的核心,为在关键应用中更高效,更易于解释的 CNN 模型铺平了道路.