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

Neural Circuits01:25

Neural Circuits

1.3K
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...
1.3K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

133
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...
133
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

193
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
193

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 authorSame journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026
Same author

SGNet: Style-Guided Network With Temporal Compensation for Unpaired Low-Light Colonoscopy Video Enhancement.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

Benchmarking Laryngeal Neoplasm Segmentation: A Multicenter Dataset and an Effective Method.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

AMLPF-CLIP: Adaptive Prompting and Distilled Learning for Imbalanced Histopathological Image Classification.

IEEE journal of biomedical and health informatics·2025
Same author

A Novel Variational Bayesian Method Based on Student's <i>t</i> Noise for Underwater Localization.

Sensors (Basel, Switzerland)·2025
Same author

Koopman-Driven Linearized Model-Based Offline Planning With Application to Freeway Ramp Metering.

IEEE transactions on neural networks and learning systems·2025
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: Jul 26, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.1K

神经推理搜索多损失细分模型

Sam Slade, Li Zhang, Haoqian Huang

    IEEE transactions on neural networks and learning systems
    |June 16, 2023
    PubMed
    概括
    此摘要是机器生成的。

    一个新的算法,神经推理搜索 (NIS),增强了深度学习语义细分模型的监视. NIS优化了超参数和多损失函数,显著提高了复杂任务和数据集的性能.

    更多相关视频

    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
    04:25

    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

    Published on: December 15, 2023

    2.5K
    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    42.7K

    相关实验视频

    Last Updated: Jul 26, 2025

    Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
    10:25

    Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

    Published on: September 25, 2019

    48.1K
    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
    04:25

    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

    Published on: December 15, 2023

    2.5K
    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    42.7K

    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:

    • 语义细分对于监控至关重要,但目前的模型在复杂的环境中缺乏所需的容忍度.
    • 现有的深度学习细分模型与多类任务和各种环境条件作斗争.

    研究的目的:

    • 引入一种新的算法,神经推理搜索 (NIS),用于优化深度学习语义细分模型.
    • 通过同时优化学习和多损失参数来提高模型性能.

    主要方法:

    • 开发了用于超参数优化的神经推理搜索 (NIS) 算法.
    • 整合了三种新的搜索行为:最大化标准偏差速度预测,本地最佳速度预测和n维旋流搜索.
    • 利用调度机制来管理搜索行为贡献,并使用长短期记忆 (LSTM) - 卷积神经网络 (CNN) 进行预测.

    主要成果:

    • 经过NIS优化的模型在五个细分数据集的多个性能指标中显示出显著的改进.
    • 与最先进的细分方法和与其他搜索算法优化的模型相比,取得了优异的结果.
    • 对于数值基准函数,NIS总是比各种搜索方法提供更好的解决方案.

    结论:

    • 神经推理搜索 (NIS) 为优化深度学习语义细分模型提供了一种强大而有效的方法.
    • 拟议的算法显著提升了语义细分的功能,用于要求严格的监控应用程序.
    • NIS提供了一种可靠的方法来提高模型性能和解决复杂的优化问题.