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相关概念视频

Time-Series Graph00:54

Time-Series Graph

4.3K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.3K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

305
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
305
Ogive Graph01:07

Ogive Graph

5.6K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
5.6K
Multiple Bar Graph01:07

Multiple Bar Graph

5.1K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
5.1K
Action Potential01:31

Action Potential

7.9K
Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they...
7.9K

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相关实验视频

Updated: Jun 16, 2025

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
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Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

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用更少的东西做得更好:对预训练图形神经网络的数据主动视角

Jiarong Xu1, Renhong Huang2, Xin Jiang3

  • 1Fudan University.

Advances in neural information processing systems
|August 15, 2024
PubMed
概括

"大数据的诅咒"影响了图形预训练 (GPT). 一个新的数据主动图表预训练 (APT) 框架使用更少,选择的数据点,以提高图形神经网络 (GNN) 的性能.

科学领域:

  • 机器学习 机器学习
  • 图形神经网络的神经网络
  • 人工智能的人工智能

背景情况:

  • 在图形神经网络 (GNN) 上进行图形预训练 (GPT),从未标记的数据中学习可转移的知识.
  • 模型的成功通常与大型数据集有关,但本研究质疑这一假设.
  • "大数据的诅咒"现象表明,更多的数据并不总是改善GNN预培训.

研究的目的:

  • 在GNN预培训中解决"大数据的诅咒".
  • 为了更有效地进行图形预训练,引入一个"更好更少"的框架.
  • 通过优化数据选择来提高下游任务性能.

主要方法:

  • 提出数据主动图表预培训 (APT) 框架.
  • 使用图形选择器来识别具有代表性和教学意义的数据点.
  • 从预培训模型中纳入预测不确定性,以指导数据选择.

主要成果:

  • APT框架通过更少的培训数据实现更好的下游性能.
  • 通过选择高质量的数据点来证明有效的预培训.
  • 突出了预测不确定性的有效性,以指导选择过程.

更多相关视频

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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Modeling the Functional Network for Spatial Navigation in the Human Brain

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相关实验视频

Last Updated: Jun 16, 2025

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Modeling the Functional Network for Spatial Navigation in the Human Brain

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结论:

  • APT框架为GNN预培训提供了一种有效的方法.
  • 仔细选择数据,以预测不确定性为指导,克服了大数据集的局限性.
  • 这种方法可以为各种下游应用程序改进GNN模型.