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

Though network pruning receives popularity in reducing the complexity of convolutional
neural networks (CNNs), it remains an open issue to concurrently maintain model accuracy …

Many recent works have shown trainability plays a central role in neural network pruning--
unattended broken trainability can lead to severe under-performance and unintentionally …

Deep neural networks (DNNs) have achieved great success in many object detection tasks.
However, such DNNS-based large object detection models are generally computationally …

People usually believe that network pruning not only reduces the computational cost of deep
networks, but also prevents overfitting by decreasing model capacity. However, our work …

The state of neural network pruning has been noticed to be unclear and even confusing for a
while, largely due to" a lack of standardized benchmarks and metrics"[3]. To standardize …

Ensemble Learning is an effective method for improving generalization in machine learning.
However, as state-of-the-art neural networks grow larger, the computational cost associated …

Automatic modulation recognition (AMR) plays an important role in communication system.
With the expansion of data volume and the development of computing power, deep learning …

Despite achieving remarkable efficiency, traditional network pruning techniques often follow
manually-crafted heuristics to generate pruned sparse networks. Such heuristic pruning …

State-of-the-art deep learning models have a parameter count that reaches into the billions.
Training, storing and transferring such models is energy and time consuming, thus costly. A …