5 papers
Metapath- and Entity-aware Graph Neural Network for Recommendation
Muhammad Umer Anwaar, Zhiwei Han, Shyam Arumugaswamy +6
In graph neural networks (GNNs), message passing iteratively aggregates nodes' information from their direct neighbors while neglecting the sequential nature of multi-hop node conn…
Joint Learning of Discriminative Low-dimensional Image Representations Based on Dictionary Learning and Two-layer Orthogonal Projections
Xian Wei, Hao Shen, Yuanxiang Li +4
There are some inadequacies in the language description of this paper that require further improvement. This paper is based on a revision of a conference paper. It is now necessary…
Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations
Xian Wei, Hao Shen, Martin Kleinsteuber
This work studies the problem of learning appropriate low dimensional image representations. We propose a generic algorithmic framework, which leverages two classic representation…
Dynamic Variational Autoencoders for Visual Process Modeling
Alexander Sagel, Hao Shen
This work studies the problem of modeling visual processes by leveraging deep generative architectures for learning linear, Gaussian representations from observed sequences. We pro…
Averaging Complex Subspaces via a Karcher Mean Approach
Knut Hüper, Martin Kleinsteuber, Hao Shen
We propose a conjugate gradient type optimization technique for the computation of the Karcher mean on the set of complex linear subspaces of fixed dimension, modeled by the so-cal…