Results 61 to 70 of about 505,774 (261)

Development and Application of Convolutional Neural Network Model

Jisuanji kexue yu tansuo, 2021
Deep learning is the latest trend of machine learning and artificial intelligence research. As a new field of rapid development in the past ten years, more and more researchers pay attention to it.
YAN Chunman ,WANG Cheng
doaj   +1 more source

Dependency-based Convolutional Neural Networks for Sentence Embedding

, 2015
In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies. To exploit both deep learning
Huang, Liang, Ma, Mingbo, Xiang, Bing, Zhou, Bowen   +3 more
core   +1 more source

Symplectic convolutional neural networks

We propose a new symplectic convolutional neural network (CNN) architecture by leveraging symplectic neural networks, proper symplectic decomposition, and tensor techniques. Specifically, we first introduce a mathematically equivalent form of the convolution layer and then, using symplectic neural networks, we demonstrate a way to parameterize the ...
Yildiz, S., Janik, K., Benner, P. ; https://orcid.org/0000-0003-3362-4103   +2 more
openaire   +3 more sources

Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling

Advanced Healthcare Materials, EarlyView.
Emerging advances in three‐dimensional bioprinting and computational modeling are reshaping cardiovascular (CV) research by enabling more realistic, patient‐specific tissue platforms. This review surveys cutting‐edge approaches that merge biomimetic CV constructs with computational simulations to overcome the limitations of traditional models, improve ...
Tanmay Mukherjee, Mehdi Salar Amoli, Sarah Rezapourdamanab, Lama Rita El Shammas, Martin L. Tomov, Emilio A. Mendiola, Vahid Serpooshan, Reza Avazmohammadi   +7 more
wiley   +1 more source

LCNN: Lookup-based Convolutional Neural Network

, 2017
Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for convolutional neural networks that enables efficient ...
Bagherinezhad, Hessam, Farhadi, Ali, Rastegari, Mohammad   +2 more
core   +1 more source

Packed for Ossification: High‐Density Bioprinting of hPDC Spheroids in HAMA Toward Endochondral Ossification

Advanced Healthcare Materials, EarlyView.
Human periosteum‐derived cell spheroids bioprinted at high density within a hyaluronic acid matrix promote fusion and hypertrophic cartilage formation in vitro. Early encapsulation enhances spheroid interaction and matrix maturation, generating scalable cartilage templates intended for endochondral bone regeneration.
Ane Albillos Sanchez, Maria Paula Marks, Paula Casademunt, Adrián Seijas‐Gamardo, Ioannis Papantoniou, Lorenzo Moroni, Carlos Mota   +6 more
wiley   +1 more source

A classification method of underwater target radiated noise signals based on enhanced images and convolutional neural networks

Frontiers in Physics
As the economy and society continue to develop, the range of underwater vehicles is expanding and technology is constantly being upgraded. Consequently, it is becoming increasingly difficult to classify and identify them, and the traditional ...
Yuan Muye, Lei Zhufeng, Dang Yinuo, Shi Zanrong   +3 more
doaj   +1 more source

Hardware implementation of a convolutional neural network using calculations in the residue number system [PDF]

Компьютерная оптика, 2019
Modern convolutional neural networks architectures are very resource intensive which limits the possibilities for their wide practical application.
Nikolay Chervyakov, Pavel Lyakhov, Nikolay Nagornov, Maria Valueva, Georgiy Valuev   +4 more
doaj   +1 more source

Isointense infant brain MRI segmentation with a dilated convolutional neural network [PDF]

, 2017
Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D ...
Moeskops, Pim, Pluim, Josien P. W.
core   +2 more sources

Location Dependency in Video Prediction

, 2018
Deep convolutional neural networks are used to address many computer vision problems, including video prediction. The task of video prediction requires analyzing the video frames, temporally and spatially, and constructing a model of how the environment ...
Azizi, Niloofar, Behnke, Sven, Farazi, Hafez   +2 more
core   +1 more source