Neural processes are a family of probabilistic models that inherit the
f...
Multi-omics data analysis has the potential to discover hidden molecular...
Contrastive learning has become a key component of self-supervised learn...
High-throughput molecular profiling technologies have produced
high-dime...
We propose a unified framework for adaptive connection sampling in graph...
Stochastic recurrent neural networks with latent random variables of com...
Temporal networks representing a stream of timestamped edges are seeming...
Representation learning over graph structured data has been mostly studi...
Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to ex...
A communication network can be modeled as a directed connected graph wit...
Networks are a natural representation of complex systems across the scie...
As mobile devices become more and more popular, mobile gaming has emerge...
Recently, considerable research attention has been paid to network embed...
Mobile gaming has emerged as a promising market with billion-dollar reve...
Bipartite graph data increasingly occurs as a stream of edges that repre...
From social science to biology, numerous applications often rely on grap...