在离散的随机结构中,图形神经网络 (GNN) 的性能障碍
1Operations Research Center, Statistics and Data Science Center, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02140.
概括
图形神经网络 (GNN) 面临着随机组合优化问题的基本限制,原因是重叠间隙属性. 这些局限性限制了GNN的性能,这表明以前的算法在某些问题实例中仍然优越.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 组合优化的优化.
背景情况:
- 图形神经网络 (GNN) 已被提议用于组合优化.
- 最近的研究讨论了在随机问题实例上与既定方法对比的GNN性能.
- 评论强调,简单的贪算法在特定基准指标中表现优于GNN.
研究的目的:
- 建立GNN的基本限制,当应用到组合优化问题的随机实例时.
- 分析GNN架构,特别是深度对性能的影响.
- 确定重叠差距属性 (OGP) 在限制GNN疗效方面的作用.
主要方法:
- 在随机图形实例上对GNN进行理论分析.
- 关于图形大小和架构参数的GNN限制的调查.
- 检查重叠差距属性 (OGP) 阶段过渡的影响.
主要成果:
- 在随机实例中,当GNN深度不与图形大小进行缩放时,GNN存在一个基本的限制.
- 这种限制不管其他GNN架构参数如何,都适用.
- 叠加差距属性 (OGP) 对 GNN 构成了障碍,类似于其他本地算法.
结论:
- 在这些特定问题实例中,GNN显示出超越现有算法的有限潜力.
- 之前的算法在OGP阶段过渡之前表现出卓越的性能.
- 这些发现支持这样的结论:在研究的问题上,GNN并非普遍优越.
更多相关视频
11:18Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
10.3K
10:44Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
2.2K
相关概念视频
Multiple Bar Graph
5.2K
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...
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...
5.2K
Signal Flow Graphs
235
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
235
Bar Graph
16.6K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
16.6K
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...
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
