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

Reducing Line Loss01:18

Reducing Line Loss

150
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
150
Survival Tree01:19

Survival Tree

79
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...
79
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

193
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
193
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

677
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...
677
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

516
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...
516

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

Hydrophobic liquid electrolyte interphases for efficient aqueous zinc batteries.

Nature nanotechnology·2026
Same author

Active Learning-Based Prediction of Drug Combination Efficacy.

ACS nano·2025
Same author

A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Machine Learning Big Data Set Analysis Reveals C-C Electro-Coupling Mechanism.

Journal of the American Chemical Society·2024
Same author

Pan-cancer analysis and experimental validation reveal FAM72D as a potential novel biomarker and therapeutic target in lung adenocarcinoma.

Gene·2024
Same author

The genetic spectrum of <i>NF1</i> variants in 10 unrelated Chinese families with neurofibromatosis type 1.

Neurosciences (Riyadh, Saudi Arabia)·2024
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jun 23, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

基于影响函数的第二阶段道修剪:评估修剪的真损失变化是可能的,而不需要重新训练.

Hongrong Cheng, Miao Zhang, Javen Qinfeng Shi

    IEEE transactions on pattern analysis and machine intelligence
    |June 18, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种用于神经网络中通道修剪的新方法. 它准确地估计了无需重新训练的性能损失,从而使更可靠的通道选择成为有效的模型压缩.

    更多相关视频

    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
    07:34

    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

    Published on: March 25, 2014

    9.9K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.3K

    相关实验视频

    Last Updated: Jun 23, 2025

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    6.8K
    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
    07:34

    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

    Published on: March 25, 2014

    9.9K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.3K

    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:

    • 频道修剪对于高效的神经网络压缩至关重要.
    • 目前的方法依赖于缓慢的再培训或不准确的性能损失估计.

    研究的目的:

    • 开发一种技术来评估真正的损失变化,而不需要再培训.
    • 为了实现可靠和自信的道选择进行修剪.

    主要方法:

    • 通过使用影响函数,推导出使用影响函数的真损失变化闭式估计器.
    • 重用影响功能从可靠的统计数据来评估对真实损失变化的影响.
    • 开发了一种基于同时评估道重要性的新型全球道修剪算法.

    主要成果:

    • 拟议的算法显著优于竞争的道修剪方法.
    • 在图像分类和对象检测任务上证明有效.
    • 展示了评估切割的真损失变化而不需要再培训的可能性.

    结论:

    • 开发的技术允许可靠的道修剪,而不需要计算上昂贵的再培训.
    • 这一发现为神经网络修剪范式开辟了新的途径.
    • 该方法为模型压缩提供了更有效,更准确的方法.