{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/compressive-sensing/papers/6","list_of":"/task/compressive-sensing","task":"Compressive Sensing","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":6,"pages_in_order":6,"rows_per_page":100,"rows":[501,597],"of":597,"counts":{"archive_papers_tagged":597,"with_a_code_link":123,"where_syntology_ran_a_sample":17,"not_listed_spam_title":0,"listed":597,"listed_where_code_ran":17,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":14,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":14,"listed_every_run_a_failure_of_syntologys_instrument":3,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/compressive-sensing","prev":"/task/compressive-sensing/papers/5","next":null,"papers":[{"url":null,"slug":"distributed-compressive-sensing-a-deep","title":"Distributed Compressive Sensing: A Deep Learning Approach","date":"2015-08-20","arxiv_id":"1508.04924","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-approach-to-structured-signal","title":"A Deep Learning Approach to Structured Signal Recovery","date":"2015-08-17","arxiv_id":"1508.04065","repositories_listed":0,"syntology":null},{"url":null,"slug":"map-support-detection-for-greedy-sparse","title":"MAP Support Detection for Greedy Sparse Signal Recovery Algorithms in Compressive Sensing","date":"2015-08-05","arxiv_id":"1508.00964","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-a-robust-sparse-data-representation","title":"Toward a Robust Sparse Data Representation for Wireless Sensor Networks","date":"2015-08-02","arxiv_id":"1508.00230","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-sparsity-discrete-and-convex","title":"Structured Sparsity: Discrete and Convex approaches","date":"2015-07-20","arxiv_id":"1507.05367","repositories_listed":0,"syntology":null},{"url":null,"slug":"ambient-occlusion-via-compressive-visibility","title":"Ambient Occlusion via Compressive Visibility Estimation","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reweighted-laplace-prior-based-hyperspectral","title":"Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pinball-loss-minimization-for-one-bit","title":"Pinball Loss Minimization for One-bit Compressive Sensing: Convex Models and Algorithms","date":"2015-05-14","arxiv_id":"1505.03898","repositories_listed":0,"syntology":null},{"url":null,"slug":"blind-compressive-sensing-framework-for","title":"Blind Compressive Sensing Framework for Collaborative Filtering","date":"2015-05-07","arxiv_id":"1505.01621","repositories_listed":0,"syntology":null},{"url":null,"slug":"fpa-cs-focal-plane-array-based-compressive","title":"FPA-CS: Focal Plane Array-based Compressive Imaging in Short-wave Infrared","date":"2015-04-16","arxiv_id":"1504.04085","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneously-sparse-and-low-rank-abundance","title":"Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing","date":"2015-04-07","arxiv_id":"1504.01515","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-bayesian-compressive-sensing-with-data","title":"Robust Bayesian compressive sensing with data loss recovery for structural health monitoring signals","date":"2015-03-28","arxiv_id":"1503.08272","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-sensing-mri-using-masked-dct-and","title":"Compressed sensing MRI using masked DCT and DFT measurements","date":"2015-03-25","arxiv_id":"1503.07384","repositories_listed":0,"syntology":null},{"url":null,"slug":"lisens-a-scalable-architecture-for-video","title":"LiSens --- A Scalable Architecture for Video Compressive Sensing","date":"2015-03-14","arxiv_id":"1503.04267","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-compressive-sensing-for-spatial","title":"Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models","date":"2015-03-09","arxiv_id":"1503.02727","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-dimensional-models-in-spatio-temporal","title":"Low-dimensional Models in Spatio-Temporal Wind Speed Forecasting","date":"2015-03-04","arxiv_id":"1503.01210","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-hyperspectral-imaging-with-side","title":"Compressive Hyperspectral Imaging with Side Information","date":"2015-02-22","arxiv_id":"1502.06260","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-algorithms-for-compressed","title":"Comparison of Algorithms for Compressed Sensing of Magnetic Resonance Images","date":"2015-02-07","arxiv_id":"1502.02182","repositories_listed":0,"syntology":null},{"url":null,"slug":"limits-on-support-recovery-with-probabilistic","title":"Limits on Support Recovery with Probabilistic Models: An Information-Theoretic Framework","date":"2015-01-29","arxiv_id":"1501.07440","repositories_listed":0,"syntology":null},{"url":null,"slug":"separation-of-undersampled-composite-signals","title":"Separation of undersampled composite signals using the Dantzig selector with overcomplete dictionaries","date":"2015-01-20","arxiv_id":"1501.04819","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruction-free-action-inference-from","title":"Reconstruction-free action inference from compressive imagers","date":"2015-01-18","arxiv_id":"1501.04367","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-sensing-of-signals-from-a-gmm","title":"Compressive Sensing of Signals from a GMM with Sparse Precision Matrices","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-sampling-for-learning-sparse","title":"Efficient Sampling for Learning Sparse Additive Models in High Dimensions","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-sublinear-sparse-representation-using","title":"Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit","date":"2014-12-01","arxiv_id":"1412.0680","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-rank-and-sparse-matrix-decomposition-with","title":"Low-Rank and Sparse Matrix Decomposition with a-priori knowledge for Dynamic 3D MRI reconstruction","date":"2014-11-23","arxiv_id":"1411.6206","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-iteratively-reweighted-least-squares","title":"Fast Iteratively Reweighted Least Squares Algorithms for Analysis-Based Sparsity Reconstruction","date":"2014-11-18","arxiv_id":"1411.5057","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-estimation-with-generalized-beta","title":"Sparse Estimation with Generalized Beta Mixture and the Horseshoe Prior","date":"2014-11-10","arxiv_id":"1411.2405","repositories_listed":0,"syntology":null},{"url":null,"slug":"tree-structure-bayesian-compressive-sensing","title":"Tree-Structure Bayesian Compressive Sensing for Video","date":"2014-10-12","arxiv_id":"1410.3080","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stage-geometric-information-guided-image","title":"Two-stage Geometric Information Guided Image Reconstruction","date":"2014-09-26","arxiv_id":"1409.7450","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-classification-with-a-deep-network","title":"Image Classification with A Deep Network Model based on Compressive Sensing","date":"2014-09-25","arxiv_id":"1409.7307","repositories_listed":0,"syntology":null},{"url":null,"slug":"compression-restoration-re-sampling","title":"Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging","date":"2014-08-27","arxiv_id":"1408.6335","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fast-patch-dictionary-method-for-whole","title":"A fast patch-dictionary method for whole image recovery","date":"2014-08-16","arxiv_id":"1408.3740","repositories_listed":0,"syntology":null},{"url":null,"slug":"multichannel-compressive-sensing-mri-using","title":"Multichannel Compressive Sensing MRI Using Noiselet Encoding","date":"2014-07-21","arxiv_id":"1407.5536","repositories_listed":0,"syntology":null},{"url":null,"slug":"recovery-of-images-with-missing-pixels-using","title":"Recovery of Images with Missing Pixels using a Gradient Compressive Sensing Algorithm","date":"2014-06-22","arxiv_id":"1407.3695","repositories_listed":0,"syntology":null},{"url":null,"slug":"truncated-nuclear-norm-minimization-for-image","title":"Truncated Nuclear Norm Minimization for Image Restoration Based On Iterative Support Detection","date":"2014-06-11","arxiv_id":"1406.2969","repositories_listed":0,"syntology":null},{"url":null,"slug":"preconditioning-for-accelerated-iteratively","title":"Preconditioning for Accelerated Iteratively Reweighted Least Squares in Structured Sparsity Reconstruction","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"image-compressive-sensing-recovery-using","title":"Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization","date":"2014-04-30","arxiv_id":"1404.7566","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatially-directional-predictive-coding-for","title":"Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images","date":"2014-04-29","arxiv_id":"1404.7211","repositories_listed":0,"syntology":null},{"url":null,"slug":"structural-group-sparse-representation-for","title":"Structural Group Sparse Representation for Image Compressive Sensing Recovery","date":"2014-04-29","arxiv_id":"1404.7212","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-bit-compressive-sensing-with-norm","title":"One-bit compressive sensing with norm estimation","date":"2014-04-28","arxiv_id":"1404.6853","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-clustering-and-missing-data","title":"A Comparison of Clustering and Missing Data Methods for Health Sciences","date":"2014-04-22","arxiv_id":"1404.5899","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-pattern-matching-on-multispectral","title":"Compressive Pattern Matching on Multispectral Data","date":"2014-03-27","arxiv_id":"1403.6958","repositories_listed":0,"syntology":null},{"url":null,"slug":"balancing-sparsity-and-rank-constraints-in","title":"Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit","date":"2014-03-17","arxiv_id":"1403.4267","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-cost-compressive-sensing-for-color-video","title":"Low-Cost Compressive Sensing for Color Video and Depth","date":"2014-02-27","arxiv_id":"1402.6932","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-theoretic-bounds-for-adaptive","title":"Information-Theoretic Bounds for Adaptive Sparse Recovery","date":"2014-02-24","arxiv_id":"1402.5731","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-fused-compressive-sensing-1-bit","title":"Binary Fused Compressive Sensing: 1-Bit Compressive Sensing meets Group Sparsity","date":"2014-02-20","arxiv_id":"1402.5074","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-two-dimensional-group-sparsity-in","title":"Exploiting Two-Dimensional Group Sparsity in 1-Bit Compressive Sensing","date":"2014-02-20","arxiv_id":"1402.5073","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-binary-fused-compressive-sensing-using","title":"Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit","date":"2014-02-20","arxiv_id":"1402.5076","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise-analysis-for-lensless-compressive","title":"Noise Analysis for Lensless Compressive Imaging","date":"2014-02-12","arxiv_id":"1402.2720","repositories_listed":0,"syntology":null},{"url":null,"slug":"signal-reconstruction-framework-based-on","title":"Signal Reconstruction Framework Based On Projections Onto Epigraph Set Of A Convex Cost Function (PESC)","date":"2014-02-10","arxiv_id":"1402.2088","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-low-dose-x-ray-ct-reconstruction","title":"Efficient Low Dose X-ray CT Reconstruction through Sparsity-Based MAP Modeling","date":"2014-02-08","arxiv_id":"1402.1801","repositories_listed":0,"syntology":null},{"url":null,"slug":"signal-to-noise-ratio-in-lensless-compressive","title":"Signal to Noise Ratio in Lensless Compressive Imaging","date":"2014-02-04","arxiv_id":"1402.0785","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-compressive-sensing-for-dynamic-mri","title":"Video Compressive Sensing for Dynamic MRI","date":"2014-01-30","arxiv_id":"1401.7715","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiscale-shrinkage-and-levy-processes","title":"Multiscale Shrinkage and Lévy Processes","date":"2014-01-11","arxiv_id":"1401.2497","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-linear-classification-and-feature","title":"Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing","date":"2014-01-05","arxiv_id":"1401.0872","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-rate-compressive-sensing-using-side","title":"Adaptive-Rate Compressive Sensing Using Side Information","date":"2014-01-03","arxiv_id":"1401.0583","repositories_listed":0,"syntology":null},{"url":null,"slug":"speeding-up-convergence-via-sequential","title":"Speeding-Up Convergence via Sequential Subspace Optimization: Current State and Future Directions","date":"2013-12-31","arxiv_id":"1401.0159","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-explicit-thresholdingshrinkage-formulas","title":"New explicit thresholding/shrinkage formulas for one class of regularization problems with overlapping group sparsity and their applications","date":"2013-12-24","arxiv_id":"1312.6813","repositories_listed":0,"syntology":null},{"url":null,"slug":"designed-measurements-for-vector-count-data","title":"Designed Measurements for Vector Count Data","date":"2013-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dictionary-learning-based-reconstruction","title":"Dictionary-Learning-Based Reconstruction Method for Electron Tomography","date":"2013-11-22","arxiv_id":"1311.5830","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-hard-thresholding-pursuit-for","title":"Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization","date":"2013-11-22","arxiv_id":"1311.5750","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-measurement-designs-for","title":"Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach","date":"2013-11-21","arxiv_id":"1311.5599","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-parallel-compressive-imaging-architecture","title":"A Parallel Compressive Imaging Architecture for One-Shot Acquisition","date":"2013-11-04","arxiv_id":"1311.0646","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameterless-optimal-approximate-message","title":"Parameterless Optimal Approximate Message Passing","date":"2013-10-31","arxiv_id":"1311.0035","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-sensing-sar-imaging-with-multilook","title":"Compressed Sensing SAR Imaging with Multilook Processing","date":"2013-10-27","arxiv_id":"1310.7217","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-sensor-placement-and-enhanced","title":"Optimal Sensor Placement and Enhanced Sparsity for Classification","date":"2013-10-15","arxiv_id":"1310.4217","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-aware-adaptive-bi-lipschitz-embeddings","title":"Energy-aware adaptive bi-Lipschitz embeddings","date":"2013-07-12","arxiv_id":"1307.3457","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-fundamental-limits-of-recovering-tree","title":"On the Fundamental Limits of Recovering Tree Sparse Vectors from Noisy Linear Measurements","date":"2013-06-18","arxiv_id":"1306.4391","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-in-lensless-compressive-imaging","title":"Multi-view in Lensless Compressive Imaging","date":"2013-06-17","arxiv_id":"1306.3946","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-canonical-time-warping-for-the","title":"Robust Canonical Time Warping for the Alignment of Grossly Corrupted Sequences","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lensless-imaging-by-compressive-sensing","title":"Lensless Imaging by Compressive Sensing","date":"2013-05-30","arxiv_id":"1305.7181","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-sensing-of-sparse-tensors","title":"Compressive Sensing of Sparse Tensors","date":"2013-05-24","arxiv_id":"1305.5777","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-find-real-world-applications-for","title":"How to find real-world applications for compressive sensing","date":"2013-05-06","arxiv_id":"1305.1199","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-adaptive-computational-ghost","title":"Compressive adaptive computational ghost imaging","date":"2013-03-31","arxiv_id":"1304.0243","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-shift-retrieval","title":"Compressive Shift Retrieval","date":"2013-03-20","arxiv_id":"1303.4996","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-sparse-model-selection-hardness-and","title":"Group-Sparse Model Selection: Hardness and Relaxations","date":"2013-03-13","arxiv_id":"1303.3207","repositories_listed":0,"syntology":null},{"url":null,"slug":"matching-pursuit-lasso-part-ii-applications","title":"Matching Pursuit LASSO Part II: Applications and Sparse Recovery over Batch Signals","date":"2013-02-20","arxiv_id":"1302.5010","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-temporal-compressive-sensing-for","title":"Adaptive Temporal Compressive Sensing for Video","date":"2013-02-14","arxiv_id":"1302.3446","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-low-rank-and-sparse-decomposition-of","title":"Adaptive low rank and sparse decomposition of video using compressive sensing","date":"2013-02-06","arxiv_id":"1302.1610","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-bregman-divergence-and-gradient","title":"Generalized Bregman Divergence and Gradient of Mutual Information for Vector Poisson Channels","date":"2013-01-28","arxiv_id":"1301.6648","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-neural-representation-of-sparse","title":"Compressive neural representation of sparse, high-dimensional probabilities","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-sensing-mri-with-wavelet-tree","title":"Compressive Sensing MRI with Wavelet Tree Sparsity","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cprl-an-extension-of-compressive-sensing-to","title":"CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exact-and-stable-recovery-of-sequences-of","title":"Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential _1-Minimization","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-aware-label-space-dimension-reduction","title":"Feature-aware Label Space Dimension Reduction for Multi-label Classification","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"forest-sparsity-for-multi-channel-compressive","title":"Forest Sparsity for Multi-channel Compressive Sensing","date":"2012-11-20","arxiv_id":"1211.4657","repositories_listed":0,"syntology":null},{"url":null,"slug":"stable-and-robust-sampling-strategies-for","title":"Stable and robust sampling strategies for compressive imaging","date":"2012-10-08","arxiv_id":"1210.2380","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-dequantized-compressive-sensing","title":"Robust Dequantized Compressive Sensing","date":"2012-07-03","arxiv_id":"1207.0577","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-acquisition-of-dynamic-scenes","title":"Compressive Acquisition of Dynamic Scenes","date":"2012-01-23","arxiv_id":"1201.4895","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparcs-recovering-low-rank-and-sparse","title":"SpaRCS: Recovering low-rank and sparse matrices from compressive measurements","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-estimation-with-structured","title":"Sparse Estimation with Structured Dictionaries","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"group-sparse-coding-with-a-laplacian-scale","title":"Group Sparse Coding with a Laplacian Scale Mixture Prior","date":"2010-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-l1-minimization-algorithms-for-robust","title":"Fast L1-Minimization Algorithms For Robust Face Recognition","date":"2010-07-21","arxiv_id":"1007.03753","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-recovery-of-jointly-sparse-vectors","title":"Efficient Recovery of Jointly Sparse Vectors","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"non-parametric-bayesian-dictionary-learning","title":"Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruction-of-sparse-circuits-using-multi","title":"Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-signal-recovery-using-markov-random","title":"Sparse Signal Recovery Using Markov Random Fields","date":"2008-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"32406abddf26920e920cc87d1e579d4f14ae61eeb60f1c73735862ff1fb582b2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}