In this paper, we present a hypergraph neural networks (HGNN) framework for
data representation learning, which can encode high-order data correlation in a
hypergraph structure. Confronting the challenges of learning representation for
complex data in real practice, we propose to incorporate such data structure in
a hypergraph, which is more flexible on data modeling, especially when dealing
with complex data. In this method, a hyperedge convolution operation is
designed to handle the data correlation during representation learning. In this
way, traditional hypergraph learning procedure can be conducted using hyperedge
convolution operations efficiently. HGNN is able to learn the hidden layer
representation considering the high-order data structure, which is a general
framework considering the complex data correlations. We have conducted
experiments on citation network classification and visual object recognition
tasks and compared HGNN with graph convolutional networks and other traditional
methods. Experimental results demonstrate that the proposed HGNN method
outperforms recent state-of-the-art methods. We can also reveal from the
results that the proposed HGNN is superior when dealing with multi-modal data
compared with existing methods.

本研究提出了一种超图神经网络 (HGNN) 框架，可以通过超图结构编码高阶数据相关性，并使用超边卷积操作处理数据相关性，以有效地进行数据表示学习。实验结果表明，HGNN 方法优于最新的最先进的方法，并且可以处理现实世界中的复杂数据和多模数据。

超图神经网络

Hypergraph Neural Networks

Deep Neural Networks (DNNs) often struggle with one-shot learning where we
have only one or a few labeled training examples per category. In this paper,
we argue that by using side information, we may compensate the missing
information across classes. We introduce two statistical approaches for fusing
side information into data representation learning to improve one-shot
learning. First, we propose to enforce the statistical dependency between data
representations and multiple types of side information. Second, we introduce an
attention mechanism to efficiently treat examples belonging to the
'lots-of-examples' classes as quasi-samples (additional training samples) for
'one-example' classes. We empirically show that our learning architecture
improves over traditional softmax regression networks as well as
state-of-the-art attentional regression networks on one-shot recognition tasks.

本文提出了两种统计学方法，将侧边信息融合到数据表示学习中，以提高深度神经网络在一次学习中的准确性。实验证明，该学习架构在一次识别任务中表现优于传统的 softmax 回归网络和最先进的注意力回归网络。

融合附加信息提高单次学习能力

Improving One-Shot Learning through Fusing Side Information

Since about 100 years ago, to learn the intrinsic structure of data, many
representation learning approaches have been proposed, including both linear
ones and nonlinear ones, supervised ones and unsupervised ones. Particularly,
deep architectures are widely applied for representation learning in recent
years, and have delivered top results in many tasks, such as image
classification, object detection and speech recognition. In this paper, we
review the development of data representation learning methods. Specifically,
we investigate both traditional feature learning algorithms and
state-of-the-art deep learning models. The history of data representation
learning is introduced, while available resources (e.g. online course, tutorial
and book information) and toolboxes are provided. Finally, we conclude this
paper with remarks and some interesting research directions on data
representation learning.

本文综述了数据表示学习方法的发展历程，探讨了传统特征学习算法和深度学习模型。提供了有关数据表示学习的历史，可用资源（例如在线课程，教程和书籍信息）和工具箱。最后，对数据表示学习的一些有趣的研究方向进行了总结。