Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

In an effort to further advance semi-supervised generative and classification tasks, we
propose a simple yet effective training strategy called* dual pseudo training*(DPT), built …

In this paper, we propose Hypergraph-Induced Semantic Tuplet (HIST) loss for deep metric
learning that leverages the multilateral semantic relations of multiple samples to multiple …

Graphs are widely used to describe real-world objects and their interactions. Graph Neural
Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly …

This paper investigates a missing feature imputation problem for graph learning tasks.
Several methods have previously addressed learning tasks on graphs with missing features …

In existing graph data, the connection relationships often exhibit uniform weights, leading to
the model aggregating neighboring nodes with equal weights across various connection …

Graphs can model complicated interactions between entities, which naturally emerge in
many important applications. These applications can often be cast into standard graph …

Sequential recommendation systems aim to forecast the subsequent item of interest to users
by analyzing their historical behaviors. While existing approaches, which employ attention …

Recently, many semi-supervised object detection (SSOD) methods adopt teacher-student
framework and have achieved state-of-the-art results. However, the teacher network is tightly …

Kernel concept factorization (KCF) has successfully utilized kernel trick to conduct matrix
factorization in the kernel space. However, conventional KCF methods usually define kernel …