Multi-Task Joint Learning of 3D Keypoint Saliency and Correspondence Estimation

2021 ◽  
Vol 141 ◽  
pp. 103105
Author(s):  
Guangshun Wei ◽  
Long Ma ◽  
Chen Wang ◽  
Christian Desrosiers ◽  
Yuanfeng Zhou
Keyword(s):  
Joint Learning

Hierarchical LSTMs with Joint Learning for Estimating Customer Satisfaction from Contact Center Calls

2017 ◽  
Author(s):  
Atsushi Ando ◽  
Ryo Masumura ◽  
Hosana Kamiyama ◽  
Satoshi Kobashikawa ◽  
Yushi Aono
Keyword(s):  
Customer Satisfaction ◽  
Contact Center ◽  
Joint Learning

Joint Learning of Correlated Sequence Labeling Tasks Using Bidirectional Recurrent Neural Networks

2017 ◽  
Author(s):  
Vardaan Pahuja ◽  
Anirban Laha ◽  
Shachar Mirkin ◽  
Vikas Raykar ◽  
Lili Kotlerman ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Joint Learning ◽  
Sequence Labeling

Simulation of sports movement training based on machine learning and brain-computer interface

2020 ◽  
pp. 1-12
Author(s):  
Linuo Wang
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Joint Learning ◽  
Scientific Methods ◽  
Learning Framework ◽  
Brain Functions ◽  
Movement Training ◽  
Practical Effect ◽  
Machine Interface ◽  
The Brain

Injuries and hidden dangers in training have a greater impact on athletes ’careers. In particular, the brain function that controls the motor function area has a greater impact on the athlete ’s competitive ability. Based on this, it is necessary to adopt scientific methods to recognize brain functions. In this paper, we study the structure of motor brain-computer and improve it based on traditional methods. Moreover, supported by machine learning and SVM technology, this study uses a DSP filter to convert the preprocessed EEG signal X into a time series, and adjusts the distance between the time series to classify the data. In order to solve the inconsistency of DSP algorithms, a multi-layer joint learning framework based on logistic regression model is proposed, and a brain-machine interface system of sports based on machine learning and SVM is constructed. In addition, this study designed a control experiment to improve the performance of the method proposed by this study. The research results show that the method in this paper has a certain practical effect and can be applied to sports.

scholarly journals Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition

2021 ◽  
pp. 1-15
Author(s):  
Guangrun Wang ◽  
Liang Lin ◽  
Rongcong Chen ◽  
Guangcong Wang ◽  
Jiqi Zhang
Keyword(s):  
Image Recognition ◽  
Joint Learning

scholarly journals Joint Learning of Super-Resolution and Perceptual Image Enhancement for Single Image

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yifei Xu ◽  
Nuo Zhang ◽  
Li Li ◽  
Genan Sang ◽  
Yuewan Zhang ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Super Resolution ◽  
Single Image ◽  
Joint Learning

Food and Ingredient Joint Learning for Fine-Grained Recognition

2020 ◽  
pp. 1-1
Author(s):  
Chengxu Liu ◽  
Yuanzhi Liang ◽  
Yao Xue ◽  
Xueming Qian ◽  
Jianlong Fu
Keyword(s):  
Joint Learning ◽  
Fine Grained

scholarly journals Joint Learning of Model Parameters and Coefficients for Online Nonlinear Estimation

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 24026-24040
Author(s):  
Masa-Aki Takizawa ◽  
Masahiro Yukawa
Keyword(s):  
Nonlinear Estimation ◽  
Model Parameters ◽  
Joint Learning
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