Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

This paper studies the efficiency problem for visual transformers by excavating redundant
calculation in given networks. The recent transformer architecture has demonstrated its …

Current dynamic networks and dynamic pruning methods have shown their promising
capability in reducing theoretical computation complexity. However, dynamic sparse …

Current CNN-based super-resolution (SR) methods process all locations equally with
computational resources being uniformly assigned in space. However, since missing details …

In federated learning (FL), data does not leave personal devices when they are jointly
training a machine learning model. Instead, these devices share gradients, parameters, or …

Most state-of-the-art deep neural networks are overparameterized and exhibit a high
computational cost. A straightforward approach to this problem is to replace convolutional …