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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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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Necessary and Sufficient Conditions for Sparsity Pattern Recovery by Fletcher, Alyson K., Goyal, Vivek K., Rangan, Sundeep

“…he paper considers the problem of detecting the sparsity pattern of a k -sparse vector in BBR n from m random noisy measurements. …”
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Better approximations for Tree Sparsity in Nearly-Linear Time by Backurs, Arturs, Indyk, Piotr, Schmidt, Ludwig

“…The Tree Sparsity problem is defined as follows: given a node-weighted tree of size n and an integer k, output a rooted subtree of size k with maximum weight. …”
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A fast algorithm for separated sparsity via perturbed lagrangians by Madry, A, Mitrović, S, Schmidt, L

“…There is now a rich class of structured sparsity approaches that expand the modeling power of the sparsity paradigm and incorporate constraints such as group sparsity, graph sparsity, or hierarchical sparsity. …”
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A Sparsity Detection Framework for On–Off Random Access Channels by Fletcher, Alyson K., Rangan, Sundeep, Goyal, Vivek K.

“…Using recent sparsity results, we derive upper and lower bounds on the capacities of these channels. …”
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Sparsity-Promoting Calibration for GRAPPA Accelerated Parallel MRI Reconstruction by Weller, Daniel S., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, Goyal, Vivek K.

“…To improve the quality of calibration when the number of auto-calibration signal (ACS) lines is restricted, we propose a sparsity-promoting regularized calibration method that finds a GRAPPA kernel consistent with the ACS fit equations that yields jointly sparse reconstructed coil channel images. …”
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SparseRT: Accelerating Unstructured Sparsity on GPUs for Deep Learning Inference by Wang, Ziheng

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HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity by Wu, Yannan, Tsai, Po-An, Muralidharan, Saurav, Parashar, Angshuman, Sze, Vivienne, Emer, Joel

“…This paper introduces hierarchical structured sparsity (HSS), with the key insight that we can systematically represent diverse sparsity degrees by having them hierarchically composed from multiple simple sparsity patterns. …”
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Sparsity Maximization under a Quadratic Constraint with Applications in Filter Design by Wei, Dennis, Oppenheim, Alan V.

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Regularizing GRAPPA using simultaneous sparsity to recover de-noised images by Goyal, Vivek K., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, Weller, Daniel Stuart

“…This novel combination of GRAPPA and CS regularizes the GRAPPA kernel computation step using a simultaneous sparsity penalty function of the coil images. This approach can be implemented by formulating the problem as the joint optimization of the least squares fit of the kernel to the ACS lines and the sparsity of the images generated using GRAPPA with the kernel.…”
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Structural sparsity of complex networks: Bounded expansion in random models and real-world graphs by Demaine, Erik D

“…To establish structural sparsity in real-world networks, we analyze several common network models regarding their structural sparsity. …”
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A Bit-level Sparsity-aware SAR ADC with Activity-scaling for AIoT Applications by Chen, Ruicong, Kung, H. T., Chandrakasan, Anantha, Lee, Hae-Seung

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Exploiting sparsity in time-of-flight range acquisition using a single time-resolved sensor by Kirmani, Ahmed, Colaco, Andrea B., Wong, Franco N. C., Goyal, Vivek K.

“…Then, a convex optimization that exploits sparsity of the Laplacian of the depth map of a typical scene determines correspondences between spatial positions and depths. …”
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Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing by Cetin, Mujdat, Stojanovic, Ivana, Onhon, Ozben, Varshney, Kush, Samadi, Sadegh, Karl, William Clem, Willsky, Alan S.

“…This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging. …”
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Nearly Linear-Time Model-Based Compressive Sensing by Hegde, Chinmay, Indyk, Piotr, Schmidt, Ludwig

“…In particular, two main barriers arise: (i) Existing recovery algorithms involve several projections into the structured sparsity model. For several sparsity models (such as tree-sparsity), the best known model-projection algorithms run in time Ω(kn), which can be too slow for large k. …”
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Simultaneously Sparse Solutions to Linear Inverse Problems with Multiple System Matrices and a Single Observation Vector by Adalsteinsson, Elfar, Zelinski, Adam C., Goyal, Vivek K.

“…Experiments involve sparsity pattern recovery in noiseless and noisy settings and MRI RF pulse design.…”
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Sequential Compressed Sensing by Malioutov, Dmitry M., Sanghavi, Sujay R., Willsky, Alan S.

“…Existing results in compressed sensing literature have focused on characterizing the achievable performance by bounding the number of samples required for a given level of signal sparsity. However, using these bounds to minimize the number of samples requires a priori knowledge of the sparsity of the unknown signal, or the decay structure for near-sparse signals. …”
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Finding sparse, equivalent SDPs using minimal coordinate projections by Permenter, Frank Noble, Parrilo, Pablo A.

“…We present a new method for simplifying SDPs that blends aspects of symmetry reduction with sparsity exploitation. By identifying a subspace of sparse matrices that provably intersects (but doesn't necessarily contain) the set of optimal solutions, we both block-diagonalize semidefinite constraints and enhance problem sparsity for many SDPs arising in sums-of-squares optimization. …”
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Training for faster adversarial robustness verification via inducing Relu stability by Xiao, Kai Yuanqing, Tjeng, Vincent, Shafiullah, Nur Muhammad Mahi., Mądry, Aleksander

“…We demonstrate that improving weight sparsity alone already enables us to turn computationally intractable verification problems into tractable ones. …”
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