High-level applications, such as machine learning, are evolving from sim...
The terms multi-task learning and multitasking are easily confused.
Mult...
In many domains where data are represented as graphs, learning a similar...
To enhance the expressiveness and representational capacity of recurrent...
Magnetic resonance imaging is capable of producing volumetric images wit...
As deep learning methods form a critical part in commercially important
...
For a deep learning model, efficient execution of its computation graph ...
Random walks are at the heart of many existing network embedding methods...
We explore encoding brain symmetry into a neural network for a brain tum...
Graphs (networks) are ubiquitous and allow us to model entities (nodes) ...
Random walks are at the heart of many existing deep learning algorithms ...
Previous work in network analysis has focused on modeling the
mixed-memb...
There is a growing interest in joint multi-subject fMRI analysis. The
ch...
Finding the most effective way to aggregate multi-subject fMRI data is a...
The scale of functional magnetic resonance image data is rapidly increas...
From social science to biology, numerous applications often rely on grap...