Impact Statement: Graph Convolutional Networks ... and online performance of model training is vital. This paper addresses this gap by illustrating the convergence condition of GCN training and ...

ABSTRACT: This paper is concerned about studying modeling-based methods in cluster analysis to classify data elements into clusters and thus dealing with time series in view of this classification to ...

FGL employs a distributed structure to train a graph machine learning model ... which impacts the convergence of federated learning. In this study, we introduced a model aggregation algorithm for ...

In fact, some experts at the frontiers of tool development say this hammer might just herald the convergence of ... “but if there is a model that can universalize, where you can put all kinds of data ...

Next, a lightweight graph neural network model ... convergence speed and convergence effect of lightweight graph convolution are both better than standard image convolution. After the training is ...

Though we're living through a time of extraordinary innovation in GPU-accelerated machine learning, the latest research papers frequently (and prominently) feature algorithms ... represents ...

First of all, combined with the basketball motion trajectory model ... machine learning algorithm shows that the improved Q-Learning algorithm can find a path to the target faster during initial ...