Results 51 to 60 of about 52,085 (312)
Training Behavior of Sparse Neural Network Topologies
, 2019 Improvements in the performance of deep neural networks have often come through the design of larger and more complex networks. As a result, fast memory is a significant limiting factor in our ability to improve network performance.
Alford, Simon +3 more
core +1 more source
Deep deformable registration: Enhancing accuracy by fully convolutional neural net [PDF]
Pattern Recognition Letters, 2017 Deformable registration is ubiquitous in medical image analysis. Many deformable registration methods minimize sum of squared difference (SSD) as the registration cost with respect to deformable model parameters. In this work, we construct a tight upper bound of the SSD registration cost by using a fully convolutional neural network (FCNN) in the ...
Ghosal, Sayan, Ray, Nilanjan
openaire +2 more sources
International Journal of Adaptive Control and Signal Processing, EarlyView.Workflow of the parameter optimization process for ITSC fault detection, applying Differential Evolution optimization and the Smooth Pseudo Wigner‐Ville Distribution for signal processing. The optimized parameters are then used in the failure identification pipeline, which combines the signal processing with a YOLO‐based architecture for fault severity
Rafael Martini Silva +4 more
wiley +1 more source
Cells, 2020 Over a decade ago, the formation of neutrophil extracellular traps (NETs) was described as a novel mechanism employed by neutrophils to tackle infections.
Aneta Manda-Handzlik +4 more
doaj +1 more source
SACNet: Shuffling atrous convolutional U‐Net for medical image segmentation
IET Image Processing, 2023 Medical images exhibit multi‐granularity and high obscurity along boundaries. As representative work, the U‐Net and its variants exhibit two shortcomings on medical image segmentation: (a) they expand the range of reception fields by applying addition or
Shaofan Wang +3 more
doaj +1 more source
On the Depth of Deep Neural Networks: A Theoretical View
, 2015 People believe that depth plays an important role in success of deep neural networks (DNN). However, this belief lacks solid theoretical justifications as far as we know. We investigate role of depth from perspective of margin bound.
Chen, Wei +4 more
core +1 more source
Advanced Engineering Materials, EarlyView.This article provides an overview of recent advancements in bulk processing of rare‐earth‐free hard magnetic materials. It also addresses related simulation approaches at different scales. The research on rare‐earth‐free magnetic materials has increased significantly in recent years, driven by supply chain issues, environmental and social concerns, and
Daniel Scheiber, Andrea Bachmaier
wiley +1 more source
Real‐time vehicle detection using segmentation‐based detection network and trajectory prediction
IET Computer VisionThe position of vehicles is determined using an algorithm that includes two stages of detection and prediction. The more the number of frames in which the detection network is used, the more accurate the detector is, and the more the prediction network ...
Nafiseh Zarei +2 more
doaj +1 more source
Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net
, 2018 Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time. Therefore, top-performing deep models of high depth and complexity are not well suited for these applications ...
Bian, Weijie +5 more
core +1 more source