An argument for sparsity by Zeitlyn, D

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. …”

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. …”

Passive cavitation mapping with temporal sparsity constraint. by Gyöngy, M, Coviello, C

On sparsity, power-law and clustering properties of graphex processes by Caron, F, Panero, F, Rousseau, J

“…Finally, we propose a class of models within this framework where one can separately control the latent structure and the global sparsity/power-law properties of the graph.…”

Multi-index Monte Carlo: when sparsity meets sampling by Haji-Ali, A, Nobile, F, Tempone, R

Tensor-sparsity of solutions to high-dimensional elliptic partial differential equations by Dahmen, W, DeVore, R, Grasedyck, L, Süli, E

“…Since these results require knowledge of the eigenbasis of the elliptic operator considered, we propose a second “basis-free” model of tensor-sparsity and prove a regularity theorem for this second sparsity model as well. …”

Adult-born dentate granule cells promote hippocampal population sparsity by McHugh, SB, Lopes-dos-Santos, V, Gava, GP, Hartwich, K, Tam, SKE, Bannerman, DM, Dupret, D

“…Selectively activating abDGCs in their 4–7-week post-birth period increases sparsity of hippocampal population patterns, whereas suppressing abDGCs reduces this sparsity, increases principal cell firing rates and impairs novel object recognition with reduced dimensionality of the network firing structure, without affecting single-neuron spatial representations. …”

Bayesian sparsity-path-analysis of genetic association signal using generalized t priors. by Lee, A, Caron, F, Doucet, A, Holmes, C

“…For low degrees of freedom, we show that the generalized t exhibits "sparsity-prior" properties with some attractive features over other common forms of sparse priors and includes the well known double-exponential distribution as the degrees of freedom tends to infinity. …”

Exploiting sparsity in the coefficient matching conditions in sum-of-squares programming using ADMM by Zheng, Y, Fantuzzi, G, Papachristodoulou, A

“…This letter introduces an efficient first-order method based on the alternating direction method of multipliers (ADMM) to solve semidefinite programs arising from sum-of-squares (SOS) programming. We exploit the sparsity of the coefficient matching conditions when SOS programs are formulated in the usual monomial basis to reduce the computational cost of the ADMM algorithm. …”

Highly accelerated vessel-selective arterial spin labelling angiography using sparsity and smoothness constraints by Schauman, SS, Chiew, M, Okell, TW

“…<p><strong>Results:</strong></p> Relative sparsity was established as a primary factor governing the reconstruction fidelity. …”

Deep neural networks with dependent weights: Gaussian process mixture limit, heavy tails, sparsity and compressibility by Lee, H, Ayed, F, Jung, P, Lee, J, Yang, H, Caron, FLR

“…Additionally, we show that, in this regime, the weights are compressible, and some nodes have asymptotically non-negligible contributions, therefore representing important hidden features. Many sparsity-promoting neural network models can be recast as special cases of our approach, and we discuss their infinite-width limits; we also present an asymptotic analysis of the pruning error. …”

Bounds of restricted isometry constants in extreme asymptotics: Formulae for Gaussian matrices by Bah, B, Tanner, J

“…This, and related quantities, provide a mechanism by which standard eigen-analysis can be applied to topics relying on sparsity. RIC bounds have been presented for a variety of random matrices and matrix dimension and sparsity ranges. …”

Scalable design of structured controllers using chordal decomposition by Zheng, Y, Mason, R, Papachristodoulou, A

“…We first extend the chordal decomposition theorem for positive semidefinite matrices to the case of matrices with block-chordal sparsity. Then, a block-diagonal Lyapunov matrix assumption is used to convert the design of structured feedback gains into a convex problem, which inherits the sparsity pattern of the original problem. …”

An exact tree projection algorithm for wavelets by Cartis, C, Thompson, A

“…In contrast to other recently proposed algorithms which only give approximate tree projections for a given sparsity, our algorithm is guaranteed to calculate the projection exactly. …”

Lasso-type recovery of sparse representations for high-dimensional data by Meinshausen, N, Yu, B

“…However, it was recently discovered that the sparsity pattern of the Lasso estimator can only be asymptotically identical to the true sparsity pattern if the design matrix satisfies the so-called irrepresentable condition. …”

Decomposition and completion of sum-of-squares matrices by Zheng, Y, Fantuzzi, G, Papachristodoulou, A

“…We show that a subset of sparse SOS matrices with chordal sparsity patterns can be equivalently decomposed into a sum of multiple SOS matrices that are nonzero only on a principal submatrix. …”

Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models by Kock, A

“…<br/><br/> Imposing a combination of sparsity and weak sparsity on the parameters of the model we first establish an oracle inequality for the Lasso. …”

Oracle inequalities for high dimensional vector autoregressions by Kock, A, Callot, L

“…<br/><br/> Next, non-asymptotic probabilities are given for the adaptive LASSO to select the correct sparsity pattern. We then provide conditions under which the adaptive LASSO reveals the correct sparsity pattern asymptotically. …”

Distributed design for decentralized control using chordal decomposition and ADMM by Zheng, Y, Kamgarpour, M, Sootla, A, Papachristodoulou, A

“…We propose a distributed design method for decentralized control by exploiting the underlying sparsity properties of the problem. Our method is based on the chordal decomposition of sparse block matrices and the alternating direction method of multipliers (ADMM). …”