Cross-Modal Deep Variational Hashing by Liong, Venice Erin, Lu, Jiwen, Zhou, Jie, Tan, Yap Peng

“…In this paper, we propose a cross-modal deep variational hashing (CMDVH) method to learn compact binary codes for cross-modality multimedia retrieval. …”
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Feature selection methods for effective diagnosis of rotavirus infection: a comparative evaluation by Chau, V, Ngoc, DTT, My, PT, Baker, S, Pham, D, Rabaa, M, Le Thi, N

“…The study found that wrapper and embedding approaches outperform filter methods, notably for the Logistic Regression (LR) and Support Vector Machine (SVM) models, employing two machine learning models and the AUC measure. …”

Learning class-specific edges for object detection and segmentation by Prasad, M, Zisserman, A, Fitzgibbon, A, Kumar, MP, Torr, PHS

“…However, for the most part, these edge-based methods operate solely on the geometric shape of edges, treating them equally and ignoring the fact that for certain object classes, the appearance of the object on the “inside” of the edge may provide valuable recognition cues.…”

3D Modelling Using Machine Learning Technique by Zhao, Haolong

“…Machine learning methods are classified as the image rending-based methods, it has the features of low cost, flexible in application, easy to set up, which are desired in most of the application scenarios. …”
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On the use of physics in machine learning for manufacturing process inspection by Barbastathis, George, Zhang, Qihang, Pandit, Ajinkya, Tang, Wenlong, Papageorgiou, Charles, Braatz, Richard, Myerson, Allan S, Tan, Bingyao, Schmetterer, Leopold

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Extreme learning machine for classification and clustering on multiview data by Chen, Jichao

“…In this thesis, the extreme learning machine is adopted to learn a nonlinearly mapped embedding space where the intrinsic structure is preserved. …”
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Counterfactual explanations for machine learning models on heterogeneous data by Wang, Yongjie

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Deep learning based monocular visual-inertial odometry by Mahdi Abolfazli Esfahani

“…The proposed method executes in real-time and surpasses traditional and deep learning based methods in terms of robust handcrafted feature extraction. …”
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Extreme learning machine based fast object recognition by Xu, Jiantao, Zhou, Hongming, Huang, Guang-Bin

“…Extreme Learning Machine (ELM) as a type of generalized single-hidden layer feed-forward networks (SLFNs) has demonstrated its good generalization performance with extreme fast learning speed in many benchmark and real applications. …”
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Prediction of neutralising antibodies for novel coronavirus with machine learning by Kho, Jordon Junyang

“…Hence, machine learning approaches have become increasingly popular for expediting and complementing lab-based methods in the search for potential antibody candidates. …”
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Region embedding with intra and inter-view contrastive learning by Zhang, Liang, Long, Cheng, Cong, Gao

“…Unsupervised region representation learning aims to extract dense and effective features from unlabeled urban data. …”
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Learning object categories from Google’s image search by Fergus, R, Fei-Fei, L, Perona, P, Zisserman, A

“…We present an approach that can learn an object category from just its name, by utilizing the raw output of image search engines available on the Internet. …”

Tangible user interface for learning pre-school mathematics by Chua, Jing Xiang

“…Teaching concepts to young children is always challenging because each individually learn things differently. There exists many methods to teach young children but which is a better method to impart knowledge? …”
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Reinforcement learning for imbalanced vehicle booming noise classification by Yang, J, Kunte, DS, Cornelis, B, Clifton, DA

“…Our findings highlight the effectiveness of a novel reinforcement learning framework in enhancing model generalizability and robustness to noise in the context of imbalanced data, outperforming traditional methods in these aspects.…”

Machine learning for NTU canteen review analysis and recommendation by Nguyen, Duy Khanh

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Leveraging imperfect restoration for data availability attack by Huang, Yi, Styborski, Jeremy, Lyu, Mingzhi, Wang, Fan, Kong, Adams Wai Kin

“…However, existing attacks often excel against either Supervised Learning (SL) or Self-Supervised Learning (SSL) scenarios. …”
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Uncertainty-Inclusive Contrastive Learning for Leveraging Synthetic Images by Cai, Fiona X.

“…Recent advancements in text-to-image generation models have sparked a growing interest in using synthesized training data to improve few-shot learning performance. Prevailing approaches treat all generated data as uniformly important, neglecting the fact that the quality of generated images varies across different domains, datasets, and methods of generation. …”
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Autonomous learning machine for online big data analysis by Ng, Jia Yu

“…Deep Metric Learning (DML) supports the non-linearity problem faced when unsupervised learning is used; whereby multi-input data corresponds to one output. …”
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Deep learning speech enhancement in satellite radio communication by Low, Yuki Yu Jun

“…Machine learning knowledge is crucial in generating models to predict the outcome of the speech enhancement model. …”
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Using deep learning for studying spread-F in the ionosphere by Luwanga, Christopher

“…In this work we have developed an automated method for detecting Spread-F in ionograms based on machine learning (ML) algorithms. …”
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