Mixup is a neural network training method that generates new samples by linear interpolation
of multiple samples and their labels. The mixup training method has better generalization …

This work investigates proactive edge caching for device-to-device (D2D)-assisted wireless
networks, where user equipment (UE) can be selected as caching nodes to assist content …

Long-term and Large-scale Wireless Traffic Forecasting (LL-WTF) is pivotal for strategic
network management and comprehensive planning on a macro scale. However, LL-WTF poses …

The number of categories of instances in the real world is normally huge, and each instance
may contain multiple labels. To distinguish these massive labels utilizing machine learning, …

As Transformer-based models have achieved impressive performance across various time
series tasks, Long-Term Series Forecasting (LTSF) has garnered extensive attention in …

Traditional regression and prediction tasks often only provide deterministic point estimates.
To estimate the uncertainty or distribution information of the response variable, methods such …

In this paper, we find that existing online forecasting methods have the following issues: 1)
They do not consider the update frequency of streaming data and directly use labels (future …

In this paper, we find that ubiquitous time series (TS) forecasting models are prone to
severe overfitting. To cope with this problem, we embrace a de-redundancy approach to …

Semantic communication aims to extract and trans-mit the semantic information of objects to
greatly reduce the transmission of redundant information. This means that termi-nals need …

As Transformer-based models have achieved impressive performance on various time series
tasks, Long-Term Series Forecasting (LTSF) tasks have also received extensive attention …