Theory-guided machine learning to predict configurational energies of high distortion alloy systems by Huang, Xufa

“…Using recursive feature elimination, optimized based on stable weight assignment of ridge regularization, we sieved out only important descriptors in a high dimensional framework where configurational energy labels vastly exceed the number of descriptors. …”
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On strong semismoothness and superlinear convergence of complementarity problems over homogeneous cones by Nguyen, Hai Ha

“…Chapter 3 mentions the smoothing Newton continuation algorithm firstly given in the article "A combined smoothing and regularization method for monotone second-order cone complementarity problems" by S. …”
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Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures by Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J. Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., Goya-Maldonado, Roberto

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Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage by Vignolle, Gabriel A., Bauerstätter, Priska, Schönthaler, Silvia, Nöhammer, Christa, Olischar, Monika, Berger, Angelika, Kasprian, Gregor, Langs, Georg, Vierlinger, Klemens, Goeral, Katharina

“…Various explainable ML techniques—including statistical, regularization, deep learning, decision trees, and Bayesian methods—were employed to predict PHVD development and survival and to discover disease-specific protein biomarkers. …”
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从频率效应看上古汉语*-s后缀 = A study of *-s suffix in archaic Chinese based on frequency effect by 黄颖瑜 Ng, Yin Yee

“…Among these morphological processes, there are still rooms for discussion regarding the regularity of si sheng bie yi(四声别义). Many scholars agree that *-s suffix is the source for si sheng bie yi(四声别义). …”
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Online learning with kernels by Fan, Haijin

“…On the other hand, to update the kernel weight, the traditional linear or nonlinear online learning algorithms are extended to RKHS to develop types of kernel online learning algorithms, such as the kernel recursive least square algorithms and the kernel (regularized) least mean square algorithms which incorporate suitable sparsification methods to select their kernel centers. …”
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A unified framework of active transfer learning for cross-system recommendation by Zhao, Lili, Pan, Sinno Jialin, Yang, Qiang

“…Based on the framework, we propose three solutions by specifying three state-of-the-art collaborative filtering methods, namely Maximum-Margin Matrix Factorization, Regularized Low-rank Matrix Factorization, and Probabilistic Matrix Factorization. …”
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To what extent and in what ways do communities of practice facilitate learning in the context of professional development for interpreting students and practitioners? by D'Hayer, Danielle

“…The formal integration of alumni to a horizontal CoP every academic year offers regularity in the life cycle of the community and value creation to all participants. …”
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Acoustic resolution photoacoustic microscopy imaging enhancement: learning based and model based approaches by Zhang, Zhengyuan

“…Model-based algorithms formulate an optimization problem from a Bayesian perspective, which involves data-fidelity and regularization terms. Variable splitting techniques, like the HQS method, can be employed to solve this problem, iterative schemes are developed to enhance the AR-PAM image with deep PnP prior. …”
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Quantitative imaging using ultrasonic tomography based on full waveform inversion by Rao, Jing

“…The thesis also investigates the performance of FWI with limited view configurations, and applies the regularization technique performed by an adaptive threshold approach to synthesize the missing components, improving the image quality. …”
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End point detection of weld seam removal in robotic abrasive belt grinding process based on computer vision and deep learning by Murugan, Pushparaja

“…Various optimization and regularization techniques are implemented for making the framework for better generalization. …”
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Category learning in infants and adults by Sucevic, J

“…The results revealed that adults, in the same way as infants, use statistical regularities to spontaneously organise a stream of visually presented items into categories.…”

Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization by Razali, Mohd Norhisham, Ibrahim, Norizuandi, Hanapi, Rozita, Mohd Zamri, Norfarahzila, Abdul Manaf, Syaifulnizam

“…Organizations can use this data to inform workforce planning, address specific challenges in departments, and cultivate an inclusive work environment. By regularly assessing productivity data and implementing recommended strategies, organizations can enhance productivity, create a conducive work environment, and support employee well-being and growth. …”
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Impact of BPL penalty functions and PSF models in PET/CT radial spatial resolution recovery by Si, Wang Jie

“…However, OSEM often struggles with full convergence, necessitating BPL with smooth penalty functions (β values) for regularized iterative reconstruction. Since National Cancer Institute (NCI) currently practice OSEM+TOF+PSF+BPL (β 450) as the in-house protocol, this study explores alternative BPL and PSF functions in OSEM to enhance spatial resolution and determine optimal β values for NCI's routine clinical use. …”
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Habit Formation and Political Persuasion: A Behavioral and Statistical Approach by Tohidi Kalorazi, Amir

“…By leveraging entropy as an implicit measure of behavioral regularity, this study aims to uncover the intricate relationship between habit formation and digital routines. …”
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Content-based image retrieval with statistical machine learning. by Zhang, Lining.

“…By introducing a Laplacian regularizer to BMMA, semi-supervised BMMA (SemiBMMA) is also proposed to utilize the information of unlabeled samples for SVM-based RF. …”
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话语标记 “可是” 在马来西亚华语会话中的语用功能 = Pragmatic functions of discourse marker “Keshi” in conversational Malaysian Mandarin by 胡文绮 Woo, Wen Qi

“…In certain contexts of daily conversation, the original sematics of “keshi” weakens, yet this phenomenon simultaneously reveals and displays certain regularities. Previous research have investigated extensively the truth value semantics of “keshi” based on written Mandarin, however, this paper finds that the various uses of “keshi” in natural conversations can hardly be explained with such semantics. …”
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Adversarial robustness of deep reinforcement learning by Qu, Xinghua

“…Motivated by this finding, we propose a new policy distillation loss with two terms: 1) a prescription gap maximization loss aiming at simultaneously maximizing the likelihood of the action selected by the teacher policy and the entropy over the remaining actions; 2) a corresponding Jacobian regularization loss that minimizes the magnitude of gradient with respect to the input state. …”
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Time frequency analysis of functional MR images by Arun Kumar

“…Additionally, we apply the random field principle to efficiently regularize the AR(1) parameters in model-based prewhitening methods. …”
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Development of schlieren-image based flow diagnostics and analysis for supersonic jet flows by Lim, Desmond Haoxiang

“…The optical flow algorithm incorporated modern minimization methods with second order div-curl regularizer and robust penalty functions. As a result, it outperformed a competitive open source optical flow algorithm in a synthetic validation test, with an average endpoint error of 0.701. …”
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