Camera domain transfer for video-based person re-identification by Ding, Bangjie

“…Besides, feature learning based on deep learning methods is prone to overfitting on the relatively small scale video dataset. …”
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Optimising public transit using big data and machine learning by Lee, Kelvin

“…Despite decades of research on optimisation of public transit, recent advances in big data collection and machine learning methods have created new possibilities for further optimisation. …”
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Outlier detection by Li, Shukai

“…Subsequently, a set of largely violated labeling vectors are combined via multiple kernel learning methods to robustly detect the outliers. To further enhance the efficacy of our outlier detector, we also explore the use of the Maximum Volume Criterion to measure the quality of separation between the outliers and the normal patterns. …”
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Self-supervised Self2Self denoising strategy for OCT speckle reduction with a single noisy image by Ge, Chenkun, Yu, Xiaojun, Yuan, Miao, Fan, Zeming, Chen, Jinna, Shum, Perry Ping, Liu, Linbo

“…Results compared with those of the existing methods demonstrate that S2Snet not only outperforms those existing self-supervised deep learning methods but also achieves better performances than those non-deep learning ones in different cases. …”
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Memory and fluctuations in chemical dynamics by Farahvash, Ardavan

“…I discuss how the strategic application of machine learning methods can drastically reduce the number of electronic structure calculations needed to produce a complete exciton trajectory. …”
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Semantic segmentation with less annotation efforts by Zhang, Tianyi

“…To alleviate the content misalignment problem, two approaches are proposed in this thesis to regularize adversarial learning methods: the first is to embed the global structure knowledge into the feature-level adversarial learning step. …”
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The analysis of teaching quality evaluation for the college sports dance by Convolutional Neural Network model and Deep Learning by Guo, Shuqing, Yang, Xiaoming, Farizan, Noor Hamzani, Samsudin, Shamsulariffin

“…This study aims to comprehensively analyze and evaluate the quality of college physical dance education using Convolutional Neural Network (CNN) models and deep learning methods. The study introduces a teaching quality evaluation (TQE) model based on one-dimensional CNN, addressing issues such as subjectivity and inconsistent evaluation criteria in traditional assessment methods. …”
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Predicting drivers’ route trajectories in last-mile delivery using a pair-wise attention-based pointer neural network by Mo, Baichuan, Wang, Qingyi, Guo, Xiaotong, Winkenbach, Matthias, Zhao, Jinhua

“…Results from an extensive case study on real operational data from Amazon’s last-mile delivery operations in the US show that our proposed method can significantly outperform traditional optimization-based approaches and other machine learning methods (such as the Long Short-Term Memory encoder–decoder and the original pointer network) in finding stop sequences that are closer to high-quality routes executed by experienced drivers in the field. …”
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15.665B Power and Negotiation, Fall 2002 by Williams, Michele

“…You will learn and practice the technical skills and analytic frameworks that are necessary to negotiate successfully with peers from other top business schools, and you will learn methods for developing the powerful social capital you will need to rise in the executive ranks of any organization. …”
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Computational Approaches for Understanding and Redesigning Enzyme Catalysis by Karvelis, Elijah

“…The approach combined statistical mechanical path sampling algorithms and machine learning methods to identify the structural characteristics of enzyme-substrate complexes primed for successful conversion of substrate to product, which were then energetically stabilized by mutating KARI. …”
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Transforming kernel-based learners to incorporate domain knowledge from climate science by Bouabid, S

“…<p>In the face of persistent modelling and observational challenges in climate science, which hinder our understanding of the climate system, statistical machine learning has emerged as a potential ally in recent years. Modern machine learning methods promise to leverage the vast volumes of data from climate model simulations, satellite imagery, or in-situ measurements to advance our understanding of the climate system and, thereby, our ability to anticipate the adverse consequences of climate change. …”

Building occupant sensing : occupancy prediction and behavior recognition by Zhu, Qingchang

“…To achieve these goals in smart buildings, it is necessary to study the problem of occupant sensing by leveraging machine learning methods to understand occupants based on sensor signals. …”
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Distinctive antibody responses to Mycobacterium tuberculosis in pulmonary and brain infection by Spatola, M, Nziza, N, Irvine, EB, Cizmeci, D, Jung, W, Van, LH, Nhat, LTH, Ha, VTN, Phu, NH, Nghia, HDT, Thwaites, GE, Lauffenburger, DA, Fortune, S, Thuong, NTT, Alter, G

“…Antibody studies included analysis of immunoglobulin isotypes (IgG, IgM, IgA) and subclass levels (IgG1–4) and the capacity of <i>M. tuberculosis</i>-specific antibodies to bind to Fc receptors or C1q and to activate innate immune effector functions (complement and natural killer cell activation; monocyte or neutrophil phagocytosis). Machine learning methods were applied to characterize serum and CSF responses in TBM, identify prognostic factors associated with disease severity, and define the key antibody features that distinguish TBM from pulmonary TB. …”

Microbial communities: network reconstruction and control by Fu, A

“…It proposes adaptive learning methods and experimental design rules to transform PAG-inferred structures into fully identified causal models, thus enhancing our understanding of microbial dynamics and providing a systematic approach for future research in causal inference within complex biological systems. …”

Feature extraction from EEG signals and regularization for brain-computer interface by Mishuhina, Vasilisa

“…The goal of this research is to improve feature extraction and regularization of EEG signals using machine learning methods and hence achieve better results during the classification of the signals for motor imagery BCI (MI-BCI). …”
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Digital problem-based learning in health professions : systematic review and meta-analysis by the digital health education collaboration by Car, Lorainne Tudor, Kyaw, Bhone Myint, Dunleavy, Gerard, Smart, Neil A., Semwal, Monika, Rotgans, Jerome Ingmar, Low-Beer, Naomi, Campbell, James

“…We included studies that compared the effectiveness of DPBL with traditional learning methods or other forms of digital education in improving health professionals’ knowledge, skills, attitudes, and satisfaction. …”
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Natural robustness of machine learning in the open world by Wei, Hongxin

“…Secondly, classic machine learning methods are built on the i.i.d. assumption that training and testing data are independent and identically distributed. …”
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Sensor-based human activity recognition via zero-shot learning by Wang, Wei

“…For problems under this problem setting, as there are no labeled training instances belonging to the unseen classes, the zero-shot learning methods are used. We focus on three problems under this setting. …”
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Brain Informatics : 14th International Conference, BI 2021, Virtual Event, September 17-19, 2021, Proceedings / by International Conference on Brain Informatics (14th : 2021 : Online), organiser 643024, Mahmud, Mufti, editor 643025, Kaiser, M. Shamim, editor 643026, Vassanelli, Stefano, editor 643027, Dai, Qionghai, 1964-, editor 643028, Zhong, Ning, 1956-, editor 643029

Brain computer interface for post-stroke motor rehabilitation by Mane, Ravikiran Tanaji

“…Moving ahead, we analyze the classification performance of proposed and baseline deep learning architectures and traditional machine learning methods for MI detection in 25 chronic stroke patients undergoing three different BCI-based motor rehabilitation interventions for 2/4 weeks. …”
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