Few-Shot Continual Learning via Flat-to-Wide Approaches.

The existing approaches on continual learning (CL) call for a lot of samples in their training processes. Such approaches are impractical for many real-world problems having limited samples because of the overfitting problem. This article proposes a few-shot CL approach, termed flat-to-wide approach (FLOWER), where a flat-to-wide learning process finding the flat-wide minima is proposed to address the catastrophic forgetting (CF) problem.

Efficient Machine Reading Comprehension for Health Care Applications: Algorithm Development and Validation of a Context Extraction Approach.

Background: Extractive methods for machine reading comprehension (MRC) tasks have achieved comparable or better accuracy than human performance on benchmark data sets. However, such models are not as successful when adapted to complex domains such as health care. One of the main reasons is that the context that the MRC model needs to process when operating in a complex domain can be much larger compared with an average open-domain context.

Coordinating the Bidding Strategy in Multiissue Multiobject Negotiation With Single and Multiple Providers.

This paper addresses the problem of flexible procurement of multiple distinct services characterized by multiple nonfunctional characteristics, i.e., quality-of-service attributes.