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

Reinforcement Schedules01:24

Reinforcement Schedules

148
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
148
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
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...
106
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

55
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...
55
Associative Learning01:27

Associative Learning

370
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
370
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

507
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
507
Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

213
Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
213

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

Unfractionated Heparin Attenuates Histone-Induced Pulmonary Endothelial Glycocalyx Injury: A Preliminary Study on the Roles of the ROCK Pathway and Heparanase‑1.

Journal of inflammation research·2026
Same author

WLR: Well-conditioned linear reconstruction for retraining-free pruning of LLMs.

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

Common clonal hematopoiesis driver mutations have disparate effects on macrophage cytokines, clonal expansion, and atherogenesis.

JCI insight·2025
Same author

PSOSP uncovers pervasive SOS-independent prophages with distinct genomic and host traits in bacterial genomes.

iMeta·2025
Same author

Long-term prognostic value of thyroid hormone levels in chronic critical illness patients.

Annals of medicine·2025
Same author

A Case Report of <i>Mycoplasma pneumoniae</i>-induced fulminant myocarditis in a 15-year-old male leading to cardiogenic shock and electrical storm.

Frontiers in cardiovascular medicine·2024

相关实验视频

Updated: Jul 4, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

542

MCMC:通过单阶段封面增强学习进行多限制模型压缩.

Siqi Li, Jun Chen, Shanqi Liu

    IEEE transactions on neural networks and learning systems
    |January 30, 2024
    PubMed
    概括

    本研究介绍了多约束模型压缩 (MCMC),这是一个用于优化神经网络在多个硬件目标,如延迟和FLOP的自动化方法. 在不牺牲准确性的情况下,MCMC有效地减少了模型大小,并提高了边缘设备的效率.

    科学领域:

    • 人工智能的人工智能
    • 计算机科学 计算机科学
    • 机器学习 机器学习

    背景情况:

    • 神经网络是计算密集型的,限制了它们在资源受限的边缘设备上部署.
    • 现有的模型压缩技术往往专注于单一的硬件目标,在多约束的现实应用中被证明是无效的.

    研究的目的:

    • 开发一种自动化修剪方法,即多约束模型压缩 (MCMC),用于同时优化神经网络对多个硬件目标的处理.
    • 为了尽量减少对模型准确性的影响,同时减少延迟,浮点运算 (FLOP) 和内存使用.

    主要方法:

    • 提出了一个改进的多目标强化学习 (MORL) 算法:一个阶段的信封深确定性政策梯度 (DDPG).
    • 调整了DDPG算法,以确定最佳的神经网络修剪策略,减少探索时间,增加目标优先级调整的灵活性.

    主要成果:

    • 在VGG-16上,MCMC实现了80%的FLOP减少,内存节省和加速,精度提高了0.09%.
    • 对于ImageNet上的MobileNet-V1,MCMC将FLOP降低了50%,提高了速度和内存压缩,同时保持了准确性.
    • 在JETSON XAVIER NX边缘设备上,MCMC为MobileNet-V1实现了71%的FLOP减少,提高了速度,内存压缩和准确性.

    结论:

    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
    Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE
    06:57

    Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

    Published on: May 14, 2019

    10.5K

    相关实验视频

    Last Updated: Jul 4, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    542
    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
    Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE
    06:57

    Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

    Published on: May 14, 2019

    10.5K
    • MCMC提供了一种有效的自动化解决方案,用于压缩神经网络以满足多个硬件约束.
    • 拟议的单阶段包 DDPG 算法提高了边缘计算应用程序的模型修剪的效率和适应性.