Machine learning for denoising by Quek, Kenneth Joo Hong

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Software development and integration for continual reassessment method by Lim, Siew Kim.

“…This is followed by a validation study of the software after a learning process of CRM from concept to detail procedures.…”
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Towards robust sensing and recognition : from statistical learning to transfer learning by Yang, Jianfei

“…To this end, we propose a Siamese deep model with both spatial and temporal feature extractors, which discards the intrinsic noises of CSI data during feature learning. The proposed method also allows user to fine-tune the system using few samples, and thus is user-friendly. …”
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A study on open set recognition methods by Sun, Xin

“…To have a shorter running time, we proposed an OSR method, called Discriminative Loss. We combine the proposed loss function with the Softmax loss function, which is used in most Convolutional Neural Networks (CNNs), to force learned features in different classes to be close to different centroids for Gaussian modeling. …”
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Interactive learning on ECG by Zhu, Yu Ting

“…This report demonstrates the various interactive learning methods with the implementation of hardware components and software development. …”
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Multifidelity Methods for Design of Transition MetalComplexes by Janet, Jon Paul

“…Multiple sources of uncertainty that would limit the application of these methods to TM complexes are addressed. Surrogate models are trained to estimate system-specific DFT uncertainty by including data from DFT calculations with different fractions of exact exchange, and a novel uncertainty metric for data-driven discovery is proposed that quantifies the ability of ANNs to generalize to unseen data based on similarity in the learned latent space. …”
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From public health to AI safety: improving machine learning approaches by collecting, selecting, or reducing the need for high-quality data by Brauner, J

“…In the process, we develop novel machine learning (ML) methods to tackle various challenges. …”

Biometric contrastive learning for data-efficient deep learning from electrocardiographic images by Sangha, V, Khunte, A, Holste, G, Mortazavi, BJ, Wang, Z, Oikonomou, EK, Khera, R

“…We compared BCL with ImageNet initialization and general-purpose self-supervised contrastive learning for images (simCLR).</p> <p><strong>Results:&nbsp;</strong>While with 100% labeled training data, BCL performed similarly to other approaches for detecting AF/Gender/LVEF&thinsp;&lt;&thinsp;40% with an AUROC of 0.98/0.90/0.90 in the held-out test sets, it consistently outperformed other methods with smaller proportions of labeled data, reaching equivalent performance at 50% of data. …”

Visual recognition using deep learning (video captioning using deep learning) by Thong, Jing Lin

“…Thereafter, reinforcement learning techniques were used to further optimise the model. …”
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Wafer-scale nanostructured black silicon with morphology engineering via advanced Sn-assisted dry etching for sensing and solar cell applications by Wu, Shaoteng, Chen, Qimiao, Zhang, Lin, Ren, Huixue, Zhou, Hao, Hu, Liangxing, Tan, Chuan Seng

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The rise of deep learning in cyber security: Bibliometric analysis of deep learning and malware by Nur Khairani, Kamarudin, Ahmad Firdaus, Zainal Abidin, Mohd Zamri, Osman, Alanda, Alde, Erianda, Aldo, Shahreen, Kasim, Mohd Faizal, Ab Razak

“…Deep learning is a machine learning technology that allows computational models to learn via experience, mimicking human cognitive processes. …”
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The devil is in the details: an evaluation of recent feature encoding methods by Chatfield, K, Lempitsky, V, Vedaldi, A, Zisserman, A

“…While several authors have reported very good results on the challenging PASCAL VOC classification data by means of these new techniques, differences in the feature computation and learning algorithms, missing details in the description of the methods, and different tuning of the various components, make it impossible to compare directly these methods and hard to reproduce the results reported. …”

Robust partial-to-partial point cloud registration in a full range by Pan, Liang, Cai, Zhongang, Liu, Ziwei

“…Extensive experiments show that GMCNet outperforms previous state-of-the-art methods for PPR.…”
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Boundary element method for non-linear heat conduction by Tan, Yuyan

“…Heat is important in our daily lives. It warms our house, dry our clothes, heat the water and enable us to cook. …”
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Image retrieval with deep learning by Tan, Joe Chin Yong

“…The query images are distorted with the 3 methods mentioned with different values of sigma, variance and quality. …”
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Efficient rare event sampling with unsupervised normalizing flows by Asghar, Solomon, Pei, Qing-Xiang, Volpe, Giorgio, Ni, Ran

“…Classical computational methods to sample rare events remain prohibitively inefficient and are bottlenecks for enhanced samplers that require prior data. …”
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Fish classification and deep learning by Zhang, Dawei

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Transfer learning on UR robots by Yu, Xiwei

“…As neural networks and deep learning develop, researchers are continually exploring the capabilities and potential of using data-driven methods to control robots. …”
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Descriptor learning for efficient retrieval by Philbin, J, Isard, M, Sivic, J, Zisserman, A

“…Scalable, stochastic gradient methods are used for the optimization.</p> <br> <p>For the case of particular object retrieval, we demonstrate impressive gains in performance on a ground truth dataset: our learnt 32-D descriptor without spatial re-ranking outperforms a baseline method using 128-D SIFT descriptors with spatial re-ranking.…”

Online learning for search and classification by Nguyen, Thanh Tam

“…(i) Feature selection: we have investigated a number of newly supervised term weighting methods to improve the performance of text classification; (ii) Online classification: we have proposed several online learning algorithms that can be used for topic classification; (iii) Two-view online learning: we have proposed a two-view online learning algorithm, which can work on two-view datasets; (iv) Online learning-to-rank: for search engine, we have proposed an online learning-to-rank algorithm, which was to learn a scoring function to re-rank the search result.…”
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