Next point-of-interest (POI) recommendation has been a hot research topic, which enables
new paradigms for kinds of location-based services in real-world scenarios. Due to the …

With the progress of human daily interaction activities and the development of industrial
society, a large amount of media data and sensor data become accessible. Humans collect …

This article introduces a versatile multi-task learning framework (UMT-Net) and an adaptive
task weighting (ATW) training method, specifically designed for resource-constrained …

In recent years, considerable endeavors have been devoted to exploring Wi-Fi-based
sensing technologies by modeling the intricate mapping between received signals and …

This paper proposes a novel approach to semantic representation learning from multi-view
datasets, distinct from most existing methodologies which typically handle single-view data …

With the rapid growth of edge intelligence, a higher level of deep neural network computing
efficiency is required. Visual intelligence, as the core component of artificial intelligence, is …

Graph neural network-based multitask learning models on multiview graphs have achieved
acceptable results in different real-world applications. However, constructing and fine-tuning …

With the enhancement of data collection capabilities, massive streaming data have been
accumulated in numerous application scenarios. Specifically, the issue of classifying data …

Existing approaches for learning representations of time-series keep the temporal
arrangement of the time-steps intact with the presumption that the original order is the most …

Typical Convolutional Neural Networks (ConvNets) depend heavily on large amounts of
image data and resort to an iterative optimization algorithm (eg, SGD or Adam) to learn …