A review of convolutional neural network architectures and their optimizations
The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …
Binary neural networks: A survey
The binary neural network, largely saving the storage and computation, serves as a
promising technique for deploying deep models on resource-limited devices. However, the …
promising technique for deploying deep models on resource-limited devices. However, the …
[HTML][HTML] Pruning by explaining: A novel criterion for deep neural network pruning
The success of convolutional neural networks (CNNs) in various applications is
accompanied by a significant increase in computation and parameter storage costs. Recent …
accompanied by a significant increase in computation and parameter storage costs. Recent …
Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference
Channel pruning is an important method to speed up CNN model's inference. Previous filter
pruning algorithms regard importance evaluation and model fine-tuning as two independent …
pruning algorithms regard importance evaluation and model fine-tuning as two independent …
Distribution-sensitive information retention for accurate binary neural network
Abstract Model binarization is an effective method of compressing neural networks and
accelerating their inference process, which enables state-of-the-art models to run on …
accelerating their inference process, which enables state-of-the-art models to run on …
A zeroing neural dynamics based acceleration optimization approach for optimizers in deep neural networks
S Liao, S Li, J Liu, H Huang, X Xiao - Neural Networks, 2022 - Elsevier
The first-order optimizers in deep neural networks (DNN) are of pivotal essence for a
concrete loss function to reach the local minimum or global one on the loss surface within …
concrete loss function to reach the local minimum or global one on the loss surface within …
Overcoming limitation of dissociation between MD and MI classifications of breast cancer histopathological images through a novel decomposed feature-based …
M Sepahvand, F Abdali-Mohammadi - Computers in Biology and Medicine, 2022 - Elsevier
Magnification-independent (MI) classification is considered a promising method for detecting
the histopathological images of breast cancer. However, it has too many parameters for real …
the histopathological images of breast cancer. However, it has too many parameters for real …
Novel stacked input-enhanced supervised autoencoder integrated with gated recurrent unit for soft sensing
Y Tian, Y Xu, QX Zhu, YL He - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Machine learning techniques have been successfully utilized as effective soft sensing for
industrial processes. Unfortunately, as modern industrial processes become increasingly …
industrial processes. Unfortunately, as modern industrial processes become increasingly …
Pruning CNN filters via quantifying the importance of deep visual representations
The achievement of convolutional neural networks (CNNs) in a variety of applications is
accompanied by a dramatic increase in computational costs and memory requirements. In …
accompanied by a dramatic increase in computational costs and memory requirements. In …
Teacher–student knowledge distillation based on decomposed deep feature representation for intelligent mobile applications
M Sepahvand, F Abdali-Mohammadi… - Expert Systems with …, 2022 - Elsevier
According to the recent studies on feature-based knowledge distillation (KD), a student
model will not be able to imitate a teacher's behavior properly if there is a high variance …
model will not be able to imitate a teacher's behavior properly if there is a high variance …