Joint Deep Networks based Multi-source Feature Learning for QoS Prediction

2021 ◽  
pp. 1-1
Author(s):  
Youhao Xia ◽  
Ding Ding ◽  
Zhenhua Chang ◽  
Fan Li
Keyword(s):  
Feature Learning ◽  
Qos Prediction ◽  
Deep Networks

scholarly journals Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication

2021 ◽  
Vol 11 (3) ◽  
pp. 1125
Author(s):  
Htet Myet Lynn ◽  
Pankoo Kim ◽  
Sung Bum Pan
Keyword(s):  
Short Term Memory ◽  
Feature Learning ◽  
Biometric Authentication ◽  
Qrs Complex ◽  
Data Types ◽  
Ecg Signals ◽  
Data Independent Acquisition ◽  
Deep Networks ◽  
Classification Tasks ◽  
Electrocardiogram Ecg

In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can be complicated for ECG signals for which the QRS complex is absent. Thus, many studies only conduct biometric authentication tasks on ECG signals with QRS complexes, and are hindered by similar limitations. To overcome this issue, we proposed a data-independent acquisition method to facilitate highly generalizable signal processing and feature learning processes. This is achieved by enhancing random segmentation to avoid complicated fiducial feature extraction, along with auto-correlation to eliminate the phase difference due to random segmentation. Subsequently, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) deep networks is utilized to automatically learn the features associated with the signal and to perform an authentication task. The experimental results suggest that the proposed data-independent approach using a BLSTM network achieves a relatively high classification accuracy for every dataset relative to the compared techniques. Moreover, it exhibited a significantly higher accuracy rate in experiments using ECG signals without the QRS complex. The results also revealed that data-dependent methods can only perform well for specified data types and amendments of data variations, whereas the presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies.

Identification and tracking of vertebrae in ultrasound using deep networks with unsupervised feature learning

2017 ◽  
Author(s):  
Jorden Hetherington ◽  
Mehran Pesteie ◽  
Victoria A. Lessoway ◽  
Purang Abolmaesumi ◽  
Robert N. Rohling
Keyword(s):  
Feature Learning ◽  
Unsupervised Feature Learning ◽  
Deep Networks

AU-inspired Deep Networks for Facial Expression Feature Learning

2015 ◽  
Vol 159 ◽  
pp. 126-136 ◽  
Author(s):  
Mengyi Liu ◽  
Shaoxin Li ◽  
Shiguang Shan ◽  
Xilin Chen
Keyword(s):  
Facial Expression ◽  
Feature Learning ◽  
Deep Networks

scholarly journals Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning

2019 ◽  
Author(s):  
Sihui Luo ◽  
Xinchao Wang ◽  
Gongfan Fang ◽  
Yao Hu ◽  
Dapeng Tao ◽  
...  
Keyword(s):  
Heterogeneous Networks ◽  
Feature Learning ◽  
Heterogeneous Structure ◽  
Light Weight ◽  
Specific Task ◽  
Plug And Play ◽  
Common Space ◽  
Heterogeneous Architectures ◽  
Deep Model ◽  
Deep Networks

An increasing number of well-trained deep networks have been released online by researchers and developers, enabling the community to reuse them in a plug-and-play way without accessing the training annotations. However, due to the large number of network variants, such public-available trained models are often of different architectures, each of which being tailored for a specific task or dataset. In this paper, we study a deep-model reusing task, where we are given as input pre-trained networks of heterogeneous architectures specializing in distinct tasks, as teacher models. We aim to learn a multitalented and light-weight student model that is able to grasp the integrated knowledge from all such heterogeneous-structure teachers, again without accessing any human annotation. To this end, we propose a common feature learning scheme, in which the features of all teachers are transformed into a common space and the student is enforced to imitate them all so as to amalgamate the intact knowledge. We test the proposed approach on a list of benchmarks and demonstrate that the learned student is able to achieve very promising performance, superior to those of the teachers in their specialized tasks.

scholarly journals Bi-level Probabilistic Feature Learning for Deformable Image Registration

2020 ◽  
Author(s):  
Risheng Liu ◽  
Zi Li ◽  
Yuxi Zhang ◽  
Xin Fan ◽  
Zhongxuan Luo
Keyword(s):  
Image Registration ◽  
Optimization Model ◽  
State Of The Art ◽  
Feature Learning ◽  
Deformable Registration ◽  
Deep Networks ◽  
Level Model ◽  
Classification Tasks ◽  
Upper Level ◽  
Traditional Approaches

We address the challenging issue of deformable registration that robustly and efficiently builds dense correspondences between images. Traditional approaches upon iterative energy optimization typically invoke expensive computational load. Recent learning-based methods are able to efficiently predict deformation maps by incorporating learnable deep networks. Unfortunately, these deep networks are designated to learn deterministic features for classification tasks, which are not necessarily optimal for registration. In this paper, we propose a novel bi-level optimization model that enables jointly learning deformation maps and features for image registration. The bi-level model takes the energy for deformation computation as the upper-level optimization while formulates the maximum \emph{a posterior} (MAP) for features as the lower-level optimization. Further, we design learnable deep networks to simultaneously optimize the cooperative bi-level model, yielding robust and efficient registration. These deep networks derived from our bi-level optimization constitute an unsupervised end-to-end framework for learning both features and deformations. Extensive experiments of image-to-atlas and image-to-image deformable registration on 3D brain MR datasets demonstrate that we achieve state-of-the-art performance in terms of accuracy, efficiency, and robustness.

Instruction Helps and Hinders Young Children’s Feature Learning

2018 ◽  
Author(s):  
Charles Kalish ◽  
Nigel Noll
Keyword(s):  
Young Children ◽  
Feature Learning ◽  
Irrelevant Information ◽  
Diagnostic Feature ◽  
Older Children ◽  
Prediction Task ◽  
Feature Correlation ◽  
The Future

Existing research suggests that adults and older children experience a tradeoff where instruction and feedback help them solve a problem efficiently, but lead them to ignore currently irrelevant information that might be useful in the future. It is unclear whether young children experience the same tradeoff. Eighty-seven children (ages five- to eight-years) and 42 adults participated in supervised feature prediction tasks either with or without an instructional hint. Follow-up tasks assessed learning of feature correlations and feature frequencies. Younger children tended to learn frequencies of both relevant and irrelevant features without instruction, but not the diagnostic feature correlation needed for the prediction task. With instruction, younger children did learn the diagnostic feature correlation, but then failed to learn the frequencies of irrelevant features. Instruction helped older children learn the correlation without limiting attention to frequencies. Adults learned the diagnostic correlation even without instruction, but with instruction no longer learned about irrelevant frequencies. These results indicate that young children do show some costs of learning with instruction characteristic of older children and adults. However, they also receive some of the benefits. The current study illustrates just what those tradeoffs might be, and how they might change over development.

Sign in / Sign up

Export Citation Format

Share Document