scholarly journals Double Cross & Deep Network for News Recommendation

2020 ◽  
Author(s):  
Zhihong Yang ◽  
Yuewei Wu ◽  
Muqing Wu ◽  
Yulong Wang
Keyword(s):  
Deep Network ◽  
News Recommendation ◽  
Double Cross

scholarly journals Research on News Recommendation System Based on Deep Network and Personalized Needs

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Weijia Zhang ◽  
Feng Ling
Keyword(s):  
Recommendation System ◽  
Poor Performance ◽  
Good Effect ◽  
Deep Network ◽  
Network Function ◽  
User Management ◽  
System Module ◽  
Interest Model ◽  
Function Module ◽  
News Recommendation

In order to solve the problems of poor performance of the recommendation system caused by not considering the needs of users in the process of news recommendation, a news recommendation system based on deep network and personalized needs is proposed. Firstly, it analyzes the news needs of users, which is the basis of designing the system. The functions of the system module mainly include the network function module, database module, user management module, and news recommendation module. Among them, the user management module uses the deep network to set the user news interest model, inputs the news data into the model, completes the personalized needs of the news, and realizes the design of the news recommendation system. The experimental results show that the proposed system has good effect and certain advantages.

Playing the Monster on Stage

2015 ◽  
Vol 45 (1) ◽  
pp. 81-89
Author(s):  
Thomas Kilroy
Keyword(s):  
Common Thread ◽  
The Veil ◽  
Double Cross

This essay explores theatre's power to take an audience beyond the veil of civilization into an encounter with the human as monstrous. Through the mythology and theatre of the Greeks, through Shakespeare, and into contemporary plays and productions by Bond, Albee, Osborne, and Bejart, the figure of the ‘overreacher’ emerges as a common thread. In extraordinary performances in his own Talbot’s Box and Double Cross, Kilroy traces the role of the actor in exteriorizing the disturbing paradox of the monster as violation and as beauty.

Image inpainting algorithm based on double-cross curvature-driven diffusion model

2013 ◽  
Vol 33 (12) ◽  
pp. 3536-3539
Author(s):  
Donghai ZHAI ◽  
Wenjie ZUO ◽  
Weixia DUAN ◽  
Jiang YU ◽  
Tongliang LI
Keyword(s):  
Diffusion Model ◽  
Image Inpainting ◽  
Curvature Driven ◽  
Double Cross

Global Command: The Double Cross Bee Cee

1951 ◽  
Vol 23 (4) ◽  
pp. 335-342
Author(s):  
James Lea Cate
Keyword(s):  
Double Cross

Towards landmarks prediction with Deep Network

2018 ◽  
Author(s):  
Van Linh Le ◽  
M. Beurton-Aimar ◽  
A. Zemmari ◽  
N. Parisey
Keyword(s):  
Deep Network

A robust and fast multispectral pedestrian detection deep network

2021 ◽  
pp. 106990
Author(s):  
Lu Ding ◽  
Yong Wang ◽  
Robert Laganière ◽  
Dan Huang ◽  
Xinbin Luo ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Deep Network

scholarly journals ChangeGAN: A Deep Network for Change Detection in Coarsely Registered Point Clouds

2021 ◽  
Vol 6 (4) ◽  
pp. 8277-8284
Author(s):  
Balazs Nagy ◽  
Lorant Kovacs ◽  
Csaba Benedek
Keyword(s):  
Change Detection ◽  
Point Clouds ◽  
Deep Network

scholarly journals An aggregate method for thorax diseases classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bayu Adhi Nugroho
Keyword(s):  
Network Architecture ◽  
Classification Problem ◽  
Calculation Algorithm ◽  
Training Pattern ◽  
Deep Network ◽  
Network Training ◽  
Chest X Ray ◽  
Medical Image Classification ◽  
Positive Pattern

AbstractA common problem found in real-word medical image classification is the inherent imbalance of the positive and negative patterns in the dataset where positive patterns are usually rare. Moreover, in the classification of multiple classes with neural network, a training pattern is treated as a positive pattern in one output node and negative in all the remaining output nodes. In this paper, the weights of a training pattern in the loss function are designed based not only on the number of the training patterns in the class but also on the different nodes where one of them treats this training pattern as positive and the others treat it as negative. We propose a combined approach of weights calculation algorithm for deep network training and the training optimization from the state-of-the-art deep network architecture for thorax diseases classification problem. Experimental results on the Chest X-Ray image dataset demonstrate that this new weighting scheme improves classification performances, also the training optimization from the EfficientNet improves the performance furthermore. We compare the aggregate method with several performances from the previous study of thorax diseases classifications to provide the fair comparisons against the proposed method.

Sign in / Sign up

Export Citation Format

Share Document