… limited expressiveness in representation learning for each … Multimodal Representation
Learning (DMRL), which models user’… In DMRL, we employ disentangled representation learning …

… methods based on network structure representation learning usually ignore this information.
This article designs a representation learning method for network structure, which can learn …

… names or identifiers, for which sequence data is abundant eg, routing advertisement, in
which case our proposed representation learning guidelines are still applicable to some extent. …

… Research on graph representation learning has received great attention in recent years
since most data in real-world applications come in the form of graphs. High-dimensional graph …

… Representation learning methods [5] attempt to solve the abovementioned problem by
constructing an embedding for each node in a graph, ie, a mapping from a node to a low-…

… deep learning has proven able to learn good representation in … Along this line, we propose
a new representation learning … simultaneously learn the new hidden representations of users …

… a Multi-view Group Representation Learning (MGPL) framework for … group representation
learning framework can leverage multiple types of information for deep representation learning …

… This article studies the problem of learning effective … In this article, we propose a
representation learning method … We present a learning algorithm tailored to the hierarchical …

… 5.4 Learning Embedding in Cold-Start Setting We incorporate the contextual information into
representation learning via positive and negative sampling to help address the sparsity and …

… representation learning can realize principles of network medicine. The core idea is to learn
… Concretely, representation learning specifies nonlinear transformation functions that map …