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

Transformation01:26

Transformation

30
Microbial communities are dynamic environments where cell lysis releases free DNA into the surroundings. Other cells can take up this extracellular DNA through a process known as transformation.When a cell incorporates this foreign DNA into its genome, resulting in genetic modification, the process is known as transformation. Cells capable of this process are termed competent. Competence can be natural, as observed in certain bacteria and archaea, or artificially induced in the...
30
Observational Learning01:12

Observational Learning

210
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...
210
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

129
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
129
Steps in the Modeling Process01:14

Steps in the Modeling Process

241
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
241
Introduction to Learning01:18

Introduction to Learning

472
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...
472
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

94
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
94

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

A plug-and-play method for guided multi-contrast MRI reconstruction based on content/style modeling.

Medical image analysis·2026
Same author

Study design and rationale of the Visualizing Subclinical Myocardial Changes with Shear Wave Elastography in Dilated Cardiomyopathy (VISUALIZE-DCM) trial.

European heart journal. Imaging methods and practice·2026
Same author

Patient-Adaptive Echocardiography using Cognitive Ultrasound.

IEEE transactions on medical imaging·2026
Same author

Exploring three-dimensional reconstruction with Neural Radiance Field (NeRF) for coronary roadmap navigation and view-planning in X-ray coronary angiography: A feasibility study.

Computer methods and programs in biomedicine·2026
Same author

DEEP-DISORDER: Motion Correction in 3D MRI via Segment Reconstruction and Registration.

NMR in biomedicine·2026
Same author

Convolutional recurrent U-net for cardiac cine MRI reconstruction via effective spatio-temporal feature exploitation.

Medical physics·2025
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
查看所有相关文章

相关实验视频

Updated: Jul 20, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

ProactiV:在输入转换下研究深度学习模型行为

Vidya Prasad, Ruud J G van Sloun, Anna Vilanova

    IEEE transactions on visualization and computer graphics
    |August 3, 2023
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了ProactiV,这是一种用于深度学习 (DL) 模型的视觉分析方法. ProactiV帮助开发人员主动识别输入转换下的模型断裂点,改善现实世界的部署.

    更多相关视频

    A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior
    06:59

    A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior

    Published on: May 21, 2020

    4.1K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.2K

    相关实验视频

    Last Updated: Jul 20, 2025

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.5K
    A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior
    06:59

    A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior

    Published on: May 21, 2020

    4.1K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.2K

    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:

    • 深度学习 (DL) 模型提供了显著的性能增长,但其解释性较低,导致现实应用中的不可预测行为.
    • 模型故障通常源于训练数据域和部署数据域之间的差异,需要方法来识别输入转换下的断点.
    • 现有的视觉分析 (VA) 方法主要集中在每个类或实例级别的分析上,限制了在不同输入下研究全球模型行为的研究.

    研究的目的:

    • 开发一种模型不可知视觉分析 (VA) 方法,ProactiV,用于在输入转换下主动研究深度学习 (DL) 模型行为.
    • 为了将分析推广到分类任务之外,在同时发生的输入转换下提供模型行为的全球视图.
    • 为了能够识别和验证模型的断点,并获得对输入特征和模型偏差的见解.

    主要方法:

    • ProactiV采用一种新的输入优化技术来确定输入修改,从而产生所需的输出.
    • 这种优化过程生成数据,用于在输入转换下对全球和本地模型行为进行大规模分析.
    • 该方法是模型不可知,适用于各种DL架构和任务,包括分类和图像对图像翻译.

    主要成果:

    • ProactiV促进了深度学习模型断裂点的积极识别和验证.
    • 输入优化方法揭示了对所需输出至关重要的输入特征,并有助于发现模型偏差.
    • 在各种任务中表现出有效性,包括图像分类和图像对图像翻译.

    结论:

    • ProactiV提供了一种可扩展和全面的方法,以理解输入转换下的深度学习模型行为.
    • 该方法通过能够主动识别故障模式来提高模型的可解释性和稳定性.
    • ProactiV支持开发人员构建更可靠和值得信赖的深度学习系统.