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

Improving Translational Accuracy02:07

Improving Translational Accuracy

2.6K
2.6K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

164
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
164
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

85
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
85
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.6K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.6K
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K

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

Long-Term Outcomes of Glucagon-Like Peptide-1 Receptor Agonists in Patients With Peripheral Artery Disease and Type 2 Diabetes.

Journal of the American Heart Association·2026
Same author

AI-enhanced ECG for acute coronary syndrome triage: A state-of-the-art review.

Cardiovascular revascularization medicine : including molecular interventions·2026
Same author

Diagnostic Approach to Left Ventricular Hypertrophy: A Review.

US cardiology·2026
Same author

REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis.

IEEE transactions on medical imaging·2026
Same author

Outcomes of TAVR Plus TEVAR Versus TAVR Alone in Patients with Concomitant Aortic Stenosis and Thoracic Aortic Aneurysm.

Heart, lung & circulation·2026
Same author

Pathogen-specific host responses define distinct pneumonia endotypes in the human lung.

bioRxiv : the preprint server for biology·2026
Same journal

Application of ephrin-B2 loaded glycol chitosan-silk fibroin hydrogel in the treatment of diabetic refractory wounds.

Scientific reports·2026
Same journal

International expert Delphi consensus on thromboprophylaxis in metabolic and bariatric surgery.

Scientific reports·2026
Same journal

Assessing the cross-region knowledge transfer capability of selected deep learning building vectorization methods in the context of available training datasets.

Scientific reports·2026
Same journal

Feasibility and preliminary effects of outdoor versus indoor cognitive-motor therapy in women with Alzheimer's disease: A randomized single-blind pilot study.

Scientific reports·2026
Same journal

Hallmarks of social action in the vocal turn-taking of wild common marmosets (Callithrix jacchus).

Scientific reports·2026
Same journal

Role and mechanism of AOPPs-induced NOX4-mediated ferroptosis in intervertebral disc degeneration.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Jul 27, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

提高材料信息学应用在参数约束下深度学习模型的性能.

Vishu Gupta1, Alec Peltekian1, Wei-Keng Liao1

  • 1Department of Electrical and Computer Engineering, Northwestern University, Evanston, USA.

Scientific reports
|June 5, 2023
PubMed
概括
此摘要是机器生成的。

研究人员开发了一个新的深度学习框架,分支残留学习 (BRNet),用于加速材料发现. 这种方法提高了预测材料属性的准确性和训练速度,优于传统的机器学习和深度学习模型.

更多相关视频

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.1K
Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models
08:32

Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models

Published on: October 20, 2023

2.8K

相关实验视频

Last Updated: Jul 27, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.1K
Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models
08:32

Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models

Published on: October 20, 2023

2.8K

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 机器学习 (ML) 和深度学习 (DL) 通过分析复杂的数据集来加速材料发现.
  • 完全连接的深度神经网络对于材料属性预测是常见的,但由于更深层架构中的消失梯度问题而遭受性能降低.
  • 现有的方法在固定的参数约束下面临性能和效率的限制.

研究的目的:

  • 为材料科学提供深度学习的模型培训和推理提高模型培训和推理性能的架构原则.
  • 引入一个新的深度学习框架,分支残留学习网络 (BRNet),以解决传统深度神经网络的局限性.
  • 提高使用基于矢量数据表示的材料属性预测的准确性和效率.

主要方法:

  • 开发了一个通用的深度学习框架,BRNet,利用具有完全连接层的分支残留学习.
  • 输入数据包括数值向量,代表材料的基于成分的属性.
  • 训练并将BRNet模型与传统的ML和现有的DL架构进行比较,以在各种数据大小中预测材料属性.

主要成果:

  • 与传统的ML和现有的DL模型相比,BRNet模型在所有测试的数据大小中显示出明显更高的准确性.
  • 拟议的分支学习方法需要比现有的神经网络更少的参数.
  • 由于在培训阶段取得了更好的融合,BRNet实现了更快的模型培训.

结论:

  • 分支残留学习 (BRNet) 为材料属性预测提供了更准确,更有效的方法.
  • 该框架有效地克服了消失梯度问题,在固定的参数约束下实现更好的性能.
  • 通过减少计算资源提供准确的预测,BRNet加速了材料发现过程.