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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

683
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,...
683
Reducing Line Loss01:18

Reducing Line Loss

430
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 in...
430
Hyperbolas01:30

Hyperbolas

514
A hyperbola is a conic section produced when a double-napped cone is intersected by a plane at an angle steeper than the slope of the cone, such that it cuts through both nappes. This intersection yields two separate, mirror-image curves known as branches, which open away from each other along the transverse axis. The nearest points on each branch to the hyperbola’s center are termed vertices, and the distance from the center to a vertex is denoted by a. Perpendicular to the transverse...
514
Upsampling01:22

Upsampling

682
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
682
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

517
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
517
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

383
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...
383

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

Ionic Liquid-Laden Mesoporous Silica for Ultrafast Gold Recovery Via Nanoconfined Interfacial Anion Exchange.

The journal of physical chemistry letters·2026
Same author

No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks.

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

Screening and enrichment of quercetin from Tetrastigma hemsleyanum Diels & Gilg toward targeted micellar delivery for lung cancer therapy.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Dual-Responsive Polymer Nanocarriers Loaded with Active Ingredients of <i>Tetrastigma Hemsleyanum</i> Diels et Gilg for Targeted Therapy of Lung Cancer.

Biomacromolecules·2026
Same author

Heterogeneous neural blind deconvolution: A signal processing-empowered foundation feature extractor for bearing fault diagnosis.

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

Quantifying the Contribution of Intrachain Conformational Locking to Covalent Hydrogel Mechanics.

Langmuir : the ACS journal of surfaces and colloids·2026
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

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

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

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

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

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

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

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

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

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

Learning Shape Anchors for Holistic Indoor Scene Understanding.

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

相关实验视频

Updated: Mar 15, 2026

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K

超压缩:通过超功能的模型压缩.

Feng-Lei Fan, Juntong Fan, Dayang Wang

    IEEE transactions on pattern analysis and machine intelligence
    |March 13, 2026
    PubMed
    概括
    此摘要是机器生成的。

    超压缩通过通过动态系统表示参数,为大模型压缩提供了一种新的方法. 这种方法实现了显著的压缩比率,性能损失最小,不需要再培训,并提供快速的处理时间.

    相关实验视频

    Last Updated: Mar 15, 2026

    Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    2.0K

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 计算科学 计算科学

    背景情况:

    • 大型模型尺寸的指数增长超过了可用的计算资源.
    • 现有的模型压缩技术,如修剪和定量化有局限性.

    研究的目的:

    • 介绍一种名为超压缩的新型压缩技术.
    • 解决大模型尺寸和计算资源限制之间的差距.

    主要方法:

    • 超压缩表示使用动态系统作为超函数的模型参数.
    • 一个具有非理性绕线的动态系统被确定为超函数.
    • 导出理论错误界限,并应用工程优化.

    主要成果:

    • 超压缩实现了优越的压缩比率,没有后期重新训练.
    • 它提供了负担得起的推断和短的压缩时间,在一小时内压缩LLaMA2-7B.
    • 性能降低低于1%,相当于int4-量化.

    结论:

    • 超压缩为大型模型压缩提供了一个独特而有效的机制.
    • 该方法是务实的,高效的,并通过显著的尺寸缩小实现高性能.