4,799 research outputs found
Directed hypergraph neural network
To deal with irregular data structure, graph convolution neural networks have
been developed by a lot of data scientists. However, data scientists just have
concentrated primarily on developing deep neural network method for un-directed
graph. In this paper, we will present the novel neural network method for
directed hypergraph. In the other words, we will develop not only the novel
directed hypergraph neural network method but also the novel directed
hypergraph based semi-supervised learning method. These methods are employed to
solve the node classification task. The two datasets that are used in the
experiments are the cora and the citeseer datasets. Among the classic directed
graph based semi-supervised learning method, the novel directed hypergraph
based semi-supervised learning method, the novel directed hypergraph neural
network method that are utilized to solve this node classification task, we
recognize that the novel directed hypergraph neural network achieves the
highest accuracies
Analysis of the mean squared derivative cost function
In this paper, we investigate the mean squared derivative cost functions that
arise in various applications such as in motor control, biometrics and optimal
transport theory. We provide qualitative properties, explicit analytical
formulas and computational algorithms for the cost functions. We also perform
numerical simulations to illustrate the analytical results. In addition, as a
by-product of our analysis, we obtain an explicit formula for the inverse of a
Wronskian matrix that is of independent interest in linear algebra and
differential equations theory.Comment: 28 page
- …