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

Channel pruning has been broadly recognized as an effective technique to reduce the
computation and memory cost of deep convolutional neural networks. However …

Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, ie,
model performance deterioration on past tasks when learning a new task. However, the …

Regularization has long been utilized to learn sparsity in deep neural network pruning.
However, its role is mainly explored in the small penalty strength regime. In this work, we …

Recently, a new trend of exploring sparsity for accelerating neural network training has
emerged, embracing the paradigm of training on the edge. This paper proposes a novel …

The rapid development and wide utilization of object detection techniques have aroused
attention on both accuracy and speed of object detectors. However, the current state-of-the …

Convolutional neural networks (CNNs) have shown excellent performance in numerous
computer vision tasks. However, the high computational and memory demands in computer …

In seeking for sparse and efficient neural network models, many previous works investigated
on enforcing L1 or L0 regularizers to encourage weight sparsity during training. The L0 …

This work studies the sparse adversarial attack, which aims to generate adversarial
perturbations onto partial positions of one benign image, such that the perturbed image is …

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 …