An argument for sparsity by Zeitlyn, D

Nonparametric Sparsity and Regularization by Mosci, Sofia, Rosasco, Lorenzo, Santoro, Matteo, Verri, Alessandro, Villa, Silvia

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Nonparametric Sparsity and Regularization by Rosasco, Lorenzo Andrea, Villa, Silvia, Mosci, Sofia, Santoro, Matteo, Verri, Alessandro

“…Based on this intuition we propose a new notion of nonparametric sparsity and a corresponding least squares regularization scheme. …”
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The applications of sparsity in classification by Tuong, Nguyen Xuan.

“…It is where the keyword “Sparsity” comes in. Because of the generality of the definition of Sparsity, in this report, we limit our discussion to a particular meaning of sparsity in which we say that a vector is sparse if it has only few non-zero coefficients. …”
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Intrinsic Sparsity of Kantorovich solutions by Hosseini, Bamdad, Steinerberger, Stefan

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Edge-exchangeable graphs and sparsity by Campbell, Trevor David, Broderick, Tamara A

“…We demonstrate that edge-exchangeable models, unlike models that are traditionally vertex exchangeable, can exhibit sparsity. To do so, we outline a general framework for graph generative models; by contrast to the pioneering work of Caron and Fox [12], models within our framework are stationary across steps of the graph sequence. …”
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Compositional Sparsity of Learnable Functions by Poggio, Tomaso, Fraser, Maia

“…This perspective argues that compositional sparsity, or the property that a compositional function have "few" constituent functions, each depending on only a small subset of inputs, is a key principle underlying successful learning architectures. …”
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Representative Selection with Structured Sparsity by Wang, Hongxing, Kawahara, Yoshinobu, Weng, Chaoqun, Yuan, Junsong

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Sparsity driven ultrasound imaging. by Tuysuzoglu, A, Kracht, J, Cleveland, R, Çetin, M, Karl, W

“…An image formation framework for ultrasound imaging from synthetic transducer arrays based on sparsity-driven regularization functionals using single-frequency Fourier domain data is proposed. …”

Leveraging Diversity and Sparsity in Blind Deconvolution by Ahmed, Ali, Demanet, Laurent

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Sparsity and robustness in modern statistical estimation by Copenhaver, Martin Steven

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Sparsity in Machine Learning: Theory and Applications by Lahlou Kitane, Driss

“…Sparsity plays a key role in machine learning for several reasons including interpretability. …”
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Compositional Sparsity: a framework for ML by Poggio, Tomaso

“…The main claim of this perspective is that compositional sparsity of the target function, which corre- sponds to the task to be learned, is the key principle underlying machine learning. …”
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Rank-Sparsity Incoherence for Matrix Decomposition by Chandrasekaran, Venkat, Sanghavi, Sujay, Parrilo, Pablo A., Willsky, Alan S.

“…We develop a notion of rank-sparsity incoherence, expressed as an uncertainty principle between the sparsity pattern of a matrix and its row and column spaces, and we use it to characterize both fundamental identifiability as well as (deterministic) sufficient conditions for exact recovery. …”
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Sensor fault detection by sparsity optimization by Yeo, Jonathan Hoe Siang

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Exploiting sparsity for neural network verification by Newton, M, Papachristodoulou, A

“…There has been significant progress to improve the efficiency and the accuracy of these methods. We investigate the sparsity that arises in a recently proposed semi-definite programming framework to verify a fully connected feed-forward neural network. …”

LOCAL AND GLOBAL PROCESSING - ROLE OF SPARSITY by Martin, M

“…The two types of processing were compared here in four different ways, for stimuli with many and with few local elements (i.e., differing sparsities). These methods consisted of assessing naming latency, intrastimulus Stroop-like interference, intermodality Stroop-like interference, and phenomenal judgment. …”

Techniques for exploiting the sparsity of the Network Admittance matrix / by 430132 Sato, Nabuo, Tinney, W. F.

“…This paper describes some computer programing techniques for taking advantage of the sparsity of the admittance matrix. The techniques are based on two main ideas; (1) determination of a sequence of operations which results in anear minimum of memory and computing. (2) preservation of these operations for repetition. …”

Sparsity: its practical application to systems analysis/ by 278844 Brameller, A., Allan, R. N. (Ronald Norman), Hamam, Y. M.

Sparsity Increases Uncertainty Estimation in Deep Ensemble by Uyanga Dorjsembe, Ju Hong Lee, Bumghi Choi, Jae Won Song

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