Manifold regularized stochastic block model by He, Tiantian, Bai, Lu, Ong, Yew-Soon

“…To fill this void, we propose a novel SBM dubbed manifold regularized stochastic model (MrSBM) to perform the task of unsupervised learning in network data in this paper. …”
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Pseudocapacitor electrodes : regular pores matter by Fan, Hong Jin

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Higher secondary polytopes and regular plabic graphs by Galashin, Pavel, Postnikov, Alexander, Williams, Lauren

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On the clique number of a strongly regular graph by Greaves, Gary Royden Watson, Soicher, Leonard H.

“…We determine new upper bounds for the clique numbers of strongly regular graphs in terms of their parameters. These bounds improve on the Delsarte bound for infinitely many feasible parameter tuples for strongly regular graphs, including infinitely many parameter tuples that correspond to Paley graphs.…”
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Reconciling Bayesian and perimeter regularization for binary inversion by Dunbar, Oliver R. A., Dunlop, Matthew M., Elliott, Charles M., Hoang, Viet Ha, Stuart, Andrew M.

“…A central theme in classical algorithms for the reconstruction of discontinuous functions from observational data is perimeter regularization via the use of total variation. On the other hand, sparse or noisy data often demand a probabilistic approach to the reconstruction of images, to enable uncertainty quantification; the Bayesian approach to inversion, which itself introduces a form of regularization, is a natural framework in which to carry this out. …”
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Laplacian embedded regression for scalable manifold regularization by Chen, Lin, Tsang, Ivor Wai-Hung, Xu, Dong

“…In particular, the manifold regularization framework has laid solid theoretical foundations for a large family of SSL algorithms, such as Laplacian support vector machine (LapSVM) and Laplacian regularized least squares (LapRLS). …”
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TRIPs-Py: Techniques for regularization of inverse problems in python by Pasha, Mirjeta, Gazzola, Silvia, Sanderford, Connor, Ugwu, Ugochukwu O.

“…The solvers available in TRIPs-Py include direct regularization methods (such as truncated singular value decomposition and Tikhonov) and iterative regularization techniques (such as Krylov subspace methods and recent solvers for ℓ𝑝 -ℓ𝑞 formulations, which enforce sparse or edge-preserving solutions and handle different noise types). …”
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Regularizing variational methods for robust object boundary detection by Fang, Wen

“…In order to achieve a robust object detection, additional boundary constraints need to be incorporated into the variational methods to regularize the curve evolution. In this thesis, two new boundary constraints are proposed. …”
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Shortest paths without a map, but with an entropic regularizer by Bubeck, S, Coester, C, Rabani, Y

Laplacian Regularized Subspace Learning for interactive image re-ranking by Zhang, Lining., Wang, Lipo., Lin, Weisi.

“…In this paper, we propose a novel subspace learning based IR scheme by using a graph embedding framework, termed Laplacian Regularized Subspace Learning (LRSL). The LRSL method can model both within-class compactness and between-class separation by specially designing an intrinsic graph and a penalty graph in the graph embedding framework, respectively. …”
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Enhancing exemplar SVMs using part level transfer regularization by Aytar, Y, Zisserman, A

“…<br> In this paper we introduce a method of part based transfer regularization that boosts the performance of E-SVMs, with a negligible additional cost. …”

Understanding collective regularity in human mobility as a familiar stranger phenomenon by Leng, Yan, Santistevan, Dominiquo, Pentland, Alex

“…To understand the social and spatial components of familiar strangers more deeply, we study the temporal regularity and spatial structure of collective urban mobility to shed light on the mechanisms that guide these interactions. …”
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Feature extraction from EEG signals and regularization for brain-computer interface by Mishuhina, Vasilisa

“…Firstly, a novel feature weighting and regularization (FWR) method that utilizes all CSP features to avoid information loss is proposed. …”
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Initialization matters : regularizing manifold-informed initialization for neural recommendation systems by Zhang, Yinan, Li, Boyang, Liu, Yong, Wang, Hao, Miao, Chunyan

“…In this work, we propose a new initialization scheme for user and item embeddings called Laplacian Eigenmaps with Popularity-based Regularization for Isolated Data (LEPORID). LEPORID endows the embeddings with information regarding multi-scale neighborhood structures on the data manifold and performs adaptive regularization to compensate for high embedding variance on the tail of the data distribution. …”
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Surrounding-aware correlation filter for UAV tracking with selective spatial regularization by Fu, Changhong, Xiong, Weijiang, Lin, Fuling, Yue, Yufeng

“…Additionally, a selective spatial regularizer is introduced to address boundary effect. …”
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Role of vegetation on beach run-up due to regular and cnoidal waves by Noarayanan, L., Murali, K., Sundar, V.

“…The studies were carried out with regular and cnoidal waves propagating over a plane slope of 1  30 in the presence and absence of vegetation of different characteristics. …”
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Analytic regularity and polynomial approximation of stochastic, parametric elliptic multiscale PDEs by Schwab, Christoph., Hoang, Viet Ha.

“…The convergence of the polynomial chaos expansion is shown to hold ℙ-a.s. and uniformly with respect to the scale parameters εi. Regularity results for the stochastic, one-scale limiting problem are established. …”
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One-Step Replica Symmetry Breaking of Random Regular NAE-SAT II by Nam, Danny, Sly, Allan, Sohn, Youngtak

“…(One-step replica symmetry breaking of random regular NAE-SAT I, <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://arxiv.org/abs/2011.14270">arXiv:2011.14270</jats:ext-link>, 2020), we study the random regular <jats:italic>k</jats:italic>-<jats:sc>nae-sat</jats:sc> model in the condensation regime. …”
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Independence test for high dimensional data based on regularized canonical correlation coefficients by Yang, Yanrong, Pan, Guangming

“…The proposed statistic is based on the sum of regularized sample canonical correlation coefficients of X and Y. …”
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Adaptive edge-preserving color image regularization framework by partial differential equations by Zhu, Lin

“…To achieve better edge-preserving regularization performance, we have proposed a locally adaptive edge-preserving regularization framework for color images. …”
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