Computational intelligence and neuroscience
Jan 10, 2022
Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data e...
Computational intelligence and neuroscience
Dec 27, 2021
The quality of boxing video is affected by many factors. For example, it needs to be compressed and encoded before transmission. In the process of transmission, it will encounter network conditions such as packet loss and jitter, which will affect th...
Neural networks : the official journal of the International Neural Network Society
Dec 2, 2021
LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training neural networks having a sparse topology. Let the sensitivity of a network parameter be the variation of the loss function with respect to the variation of the parameter. Parame...
The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deplo...
Versatile video coding (VVC) achieves enormous improvement over the advanced high efficiency video coding (HEVC) standard due to the adoption of the quadtree with nested multi-type tree (QTMT) partition structure and other coding tools. However, the ...
Computational intelligence and neuroscience
Oct 7, 2021
Financial early warning mechanism is of great significance to the long-term healthy development and stable operation of listed enterprises. This paper adopts the logistic regression early warning model and BP neural network early warning model. Based...
Neural networks : the official journal of the International Neural Network Society
Sep 30, 2021
Pruning methods to compress and accelerate deep convolutional neural networks (CNNs) have recently attracted growing attention, with the view of deploying pruned networks on resource-constrained hardware devices. However, most existing methods focus ...
Neural networks : the official journal of the International Neural Network Society
Sep 8, 2021
Deep neural network (DNN) compression has become a hot topic in the research of deep learning since the scale of modern DNNs turns into too huge to implement on practical resource constrained platforms such as embedded devices. Among variant compress...
A common technique for compressing a neural network is to compute the -rank ℓ2 approximation Ak of the matrix A∈Rn×d via SVD that corresponds to a fully connected layer (or embedding layer). Here, is the number of input neurons in the layer, is the...
Implementing deep convolutional neural networks (CNNs) with boolean arithmetic is ideal for eliminating the notoriously high computational expense of deep learning models. However, although lossless model compression via weight-only quantization has ...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.