As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the
future, as opposed to making a diagnosis, which is concerned with the current state. During …

Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …

Meta-learning for few-shot learning entails acquiring a prior over previous tasks and
experiences, such that new tasks be learned from small amounts of data. However, a critical …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for
quickly improving performance on a novel task. Bayesian hierarchical modeling provides a …

In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic
data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive …

The high feature-dimension and low sample-size problem is one of the major challenges in
the study of computer-aided Alzheimer's disease (AD) diagnosis. To circumvent this …

Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the
efficiency of learning on a novel task. This approach encounters difficulty when transfer is …

Humans have a remarkable ability to learn new concepts from only a few examples and
quickly adapt to unforeseen circumstances. To do so, they build upon their prior experience …