In beamformed wireless cellular systems such as 5G New Radio (NR) networ...
Multivariate time series long-term prediction, which aims to predict the...
Communication in millimeter wave (mmWave) and even terahertz (THz) frequ...
Traffic forecasting, which aims to predict traffic conditions based on
h...
CLIP has become a promising language-supervised visual pre-training fram...
Deep neural networks have achieved remarkable performance for artificial...
Recently, a series of pioneer studies have shown the potency of pre-trai...
Class-Incremental Learning (CIL) aims to solve the neural networks'
cata...
Intelligent reflecting surface (IRS) has been considered as a promising
...
In this paper, we investigate and analyze energy recycling for a
reconfi...
Unlike the conventional Knowledge Distillation (KD), Self-KD allows a ne...
Multivariate Time Series (MTS) forecasting plays a vital role in a wide ...
The teacher-free online Knowledge Distillation (KD) aims to train an ens...
Multivariate Time Series (MTS) forecasting plays a vital role in a wide ...
We all depend on mobility, and vehicular transportation affects the dail...
Existing works often focus on reducing the architecture redundancy for
a...
Current Knowledge Distillation (KD) methods for semantic segmentation of...
Generative models often incur the catastrophic forgetting problem when t...
Nowadays, Knowledge graphs (KGs) have been playing a pivotal role in
AI-...
To mitigate the effects of shadow fading and obstacle blocking,
reconfig...
Knowledge distillation (KD) is an effective framework that aims to trans...
Graph neural networks for heterogeneous graph embedding is to project no...
Distributed training is an effective way to accelerate the training proc...
The underwater acoustic channel is one of the most challenging communica...
Knowledge distillation often involves how to define and transfer knowled...
We present a collaborative learning method called Mutual Contrastive Lea...
Channel pruning has demonstrated its effectiveness in compressing ConvNe...
Filter pruning is widely used to reduce the computation of deep learning...
Network pruning is widely used to compress Deep Neural Networks (DNNs). ...
The existence of a lot of redundant information in convolutional neural
...
Existing Online Knowledge Distillation (OKD) aims to perform collaborati...
Mobile edge computing (MEC) is an emerging communication scheme that aim...
Deep convolutional neural networks (CNN) always non-linearly aggregate t...
Existing approaches to improve the performances of convolutional neural
...
This paper proposes to use an interpretable method to dissect the channe...
Latest algorithms for automatic neural architecture search perform remar...
We design a highly efficient architecture called Gated Convolutional Net...
In this work, we propose a heuristic genetic algorithm (GA) for pruning
...
Latest algorithms for automatic neural architecture search perform remar...