Residual learning diagnosis detection: an advanced residual learning diagnosis detection system for COVID-19 in industrial internet of things by Zhang, Mingdong, Chu, Ronghe, Dong, Chaoyu, Wei, Jianguo, Lu, Wenhuan, Xiong, Naixue

“…Early diagnosis and isolation are effective and imperative strategies for epidemic prevention and control. Most diagnostic methods for the COVID-19 is based on nucleic acid testing (NAT), which is expensive and time-consuming. …”
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Enhanced extreme learning machines for image classification by Cui, Dongshun

“…Among numerous machine learning methods, we choose the Extreme Learning Machine (ELM) for our image classification applications. …”
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Enabling open-ended questions in team-based learning using automated marking: impact on student achievement, learning and engagement by Tan, Sophia Huey Shan, Thibault, Guillaume, Chew, Anna Chia Yin, Rajalingam, Preman

“…Methods: MCQs and OEQs test scores of N = 66 students were automatically captured in Learning Activity Management System (LAMS) and were compared using a switching replications quasi-experimental design with pre- and post-tests to answer the research questions. …”
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Learning hierarchical review graph representations for recommendation by Liu, Yong, Yang, Susen, Zhang, Yinan, Miao, Chunyan, Nie, Zaiqing, Zhang, Juyong

“…Previous review-based recommendation methods usually employ sophisticated compositional models, such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), to learn semantic representations from the review data for recommendation. …”
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Development of a learning system for robot control by Leong, Marcus Khee Ing

“…This project aims to develop a learning algorithm that can learn the Jacobian matrix of the robot with the use of neural network and control the end-effector to follow a predefined trajectory without modelling the robot kinematics. …”
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Synthesizing photorealistic images with deep generative learning by Zheng, Chuanxia

“…Extensive experiments demonstrate superior performance compared to previous CNN-based methods on several datasets. Part 3 combines recognitive learning and the latest generative modeling into a holistic scene decomposition and completion framework, where a network is trained to decompose a scene into individual objects, infer their underlying occlusion relationships, and moreover imagine what the originally occluded objects may look like, while using only a single image as input. …”
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Elementwise overparameterisation for single and multi-task learning by Ribli, Vincent

“…To solve such an issue, methods such as overparameterisation, along with multi-task learning, have been proposed. …”
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Support app UI development with deep learning by Chow, Bernard Wei Jun

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Efficient HIK SVM learning for image classification by Wu, Jianxin

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Quantum photonic sensors array with machine learning by Ho, Ping-Chien

“…DQS-CV protocol can also be used to help improve machine learning which is called the supervised learning assisted by and entangled sensor network(SLAEN).SLAEN converge in less step compared to the conventional algorithm and can find better hyperplanes. …”
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Deep learning for anomaly detection in computational imaging by Du, Xinglin

“…For supervised method, three methods are used: multilayer perceptron, convolutional neural network and transfer learning. …”
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Unsupervised Learning for Generative Scene Editing and Motion by Fang, David S.

“…Unsupervised learning for images and videos is important for many applications in computer vision. …”
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Object recognition using deep learning features by Zhu, Daiqing

“…However, previous algorithms need to train classifier to rank proposal and learn from object class recognition datasets. That is time consuming since those classifiers need to be trained again when new object category comes and database grow. …”
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Differentiable Vector Graphics Rasterization for Editing and Learning by Li, Tzu-Mao, Lukac, Mike, Gharbi, Michael, Ragan-Kelley, Jonathan

“…We introduce a differentiable rasterizer that bridges the vector graphics and raster image domains, enabling powerful raster-based loss functions, optimization procedures, and machine learning techniques to edit and generate vector content. …”
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Robustness to training disturbances in SpikeProp Learning by Shrestha, Sumit Bam, Song, Qing

“…The performance of learning using this scheme has been compared with the prevailing methods for different benchmark data sets and the results show that this method has stable learning reflected by minimal surges during learning, higher success in training instances, and faster learning as well.…”
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Vehicle re-identification using machine learning by Tang, Lisha

“…Finally, we address this Re-ID issue as a multi-task problem and employ Homoscedastic Uncertainty Learning to automatically balance the loss weightings. …”
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An analytic end-to-end collaborative learning algorithm by Li, Sitan, Cheah, Chien Chern

“…However, most current deep learning methods are black-box approaches that are more focused on empirical studies. …”
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Goal modelling for deep reinforcement learning agents by Leung, Jonathan, Shen, Zhiqi, Zeng, Zhiwei, Miao, Chunyan

“…The experiments demonstrate that our method is more sample efficient and can obtain higher average rewards than other related methods that incorporate prior human knowledge in similar ways.…”
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Intelligent diagnostic feedback in virtual learning environment by Guo, Ruisheng

“…This research compares and contrasts several methods in order to investigate the effective use of intelligent feedback towards modelling the stages of students’learning. …”
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Structured sparse representations for supervised and unsupervised learning by Zeng, Yijie

“…Specifically, a slice-based online convolutional dictionary learning (SOCDL) method is proposed for unsupervised image processing tasks such as image reconstruction and inpainting. …”
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