{"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/compressed-sensing/papers/3","list_of":"/task/compressed-sensing","task":"compressed 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":3,"pages_in_order":10,"rows_per_page":100,"rows":[201,300],"of":992,"counts":{"archive_papers_tagged":992,"with_a_code_link":246,"where_syntology_ran_a_sample":27,"not_listed_spam_title":0,"listed":992,"listed_where_code_ran":27,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":25,"every_run_a_failure_of_syntologys_instrument":2,"listed_with_a_run_with_no_instrument_failure":25,"listed_every_run_a_failure_of_syntologys_instrument":2,"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/compressed-sensing","prev":"/task/compressed-sensing/papers/2","next":"/task/compressed-sensing/papers/4","papers":[{"url":"/paper/3d-smrnet-achieving-a-new-quality-of-mpi","slug":"3d-smrnet-achieving-a-new-quality-of-mpi","title":"3d-SMRnet: Achieving a new quality of MPI system matrix recovery by deep learning","date":"2019-05-08","arxiv_id":"1905.03026","repositories_listed":1,"syntology":null},{"url":"/paper/source-coding-based-mmwave-channel-estimation","slug":"source-coding-based-mmwave-channel-estimation","title":"Source Coding Based Millimeter-Wave Channel Estimation with Deep Learning Based Decoding","date":"2019-04-30","arxiv_id":"1905.00124","repositories_listed":1,"syntology":null},{"url":"/paper/spatio-temporal-deep-learning-based","slug":"spatio-temporal-deep-learning-based","title":"Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Data","date":"2019-04-01","arxiv_id":"1904.01574","repositories_listed":1,"syntology":null},{"url":"/paper/compressed-sensing-from-research-to-clinical","slug":"compressed-sensing-from-research-to-clinical","title":"Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning","date":"2019-03-19","arxiv_id":"1903.07824","repositories_listed":1,"syntology":{"n":15,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/compressed-sensing-from-research-to-clinical#ran","syntology_url":"https://syntology.ai/paper/1903.07824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.07824"}},"official":{"repos":["MRSRL/dl-cs"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-sample-efficient-blackbox","slug":"adaptive-sample-efficient-blackbox","title":"From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization","date":"2019-03-07","arxiv_id":"1903.04268","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-convolutional-neural-networks-for","slug":"bayesian-convolutional-neural-networks-for","title":"Bayesian Convolutional Neural Networks for Compressed Sensing Restoration","date":"2019-02-24","arxiv_id":"1811.04356","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-learning-based-sampling-optimization","slug":"scalable-learning-based-sampling-optimization","title":"Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI","date":"2019-02-01","arxiv_id":"1902.00386","repositories_listed":1,"syntology":null},{"url":"/paper/a-biconvex-analysis-for-lasso-l1-reweighting","slug":"a-biconvex-analysis-for-lasso-l1-reweighting","title":"A biconvex analysis for Lasso l1 reweighting","date":"2018-12-07","arxiv_id":"1812.02990","repositories_listed":1,"syntology":null},{"url":"/paper/enhanced-expressive-power-and-fast-training","slug":"enhanced-expressive-power-and-fast-training","title":"Enhanced Expressive Power and Fast Training of Neural Networks by Random Projections","date":"2018-11-22","arxiv_id":"1811.09054","repositories_listed":1,"syntology":null},{"url":"/paper/mulan-a-blind-and-off-grid-method-for","slug":"mulan-a-blind-and-off-grid-method-for","title":"MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval","date":"2018-10-31","arxiv_id":"1810.13338","repositories_listed":1,"syntology":null},{"url":"/paper/a-hybrid-frequency-domainimage-domain-deep","slug":"a-hybrid-frequency-domainimage-domain-deep","title":"A Hybrid Frequency-domain/Image-domain Deep Network for Magnetic Resonance Image Reconstruction","date":"2018-10-30","arxiv_id":"1810.12473","repositories_listed":1,"syntology":null},{"url":"/paper/reproducing-ambientgan-generative-models-from","slug":"reproducing-ambientgan-generative-models-from","title":"Reproducing AmbientGAN: Generative models from lossy measurements","date":"2018-10-23","arxiv_id":"1810.10108","repositories_listed":1,"syntology":null},{"url":"/paper/mri-reconstruction-via-cascaded-channel-wise","slug":"mri-reconstruction-via-cascaded-channel-wise","title":"MRI Reconstruction via Cascaded Channel-wise Attention Network","date":"2018-10-18","arxiv_id":"1810.08229","repositories_listed":1,"syntology":null},{"url":"/paper/compressed-sensing-using-binary-matrices-of","slug":"compressed-sensing-using-binary-matrices-of","title":"Compressed Sensing Using Binary Matrices of Nearly Optimal Dimensions","date":"2018-08-09","arxiv_id":"1808.03001","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-and-perceptual-refinement-for","slug":"adversarial-and-perceptual-refinement-for","title":"Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction","date":"2018-06-28","arxiv_id":"1806.11216","repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-compressed-sensing-measurement","slug":"learning-a-compressed-sensing-measurement","title":"Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling","date":"2018-06-26","arxiv_id":"1806.10175","repositories_listed":1,"syntology":null},{"url":"/paper/algorithms-for-the-construction-of-incoherent","slug":"algorithms-for-the-construction-of-incoherent","title":"Algorithms for the Construction of Incoherent Frames Under Various Design Constraints","date":"2018-06-20","arxiv_id":"1801.09678","repositories_listed":1,"syntology":null},{"url":"/paper/deep-neural-network-based-sparse-measurement","slug":"deep-neural-network-based-sparse-measurement","title":"Deep neural network based sparse measurement matrix for image compressed sensing","date":"2018-06-19","arxiv_id":"1806.07026","repositories_listed":1,"syntology":null},{"url":"/paper/compressed-sensing-with-deep-image-prior-and","slug":"compressed-sensing-with-deep-image-prior-and","title":"Compressed Sensing with Deep Image Prior and Learned Regularization","date":"2018-06-17","arxiv_id":"1806.06438","repositories_listed":1,"syntology":null},{"url":"/paper/neural-proximal-gradient-descent-for","slug":"neural-proximal-gradient-descent-for","title":"Neural Proximal Gradient Descent for Compressive Imaging","date":"2018-06-01","arxiv_id":"1806.03963","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-learning-with-steins-unbiased","slug":"unsupervised-learning-with-steins-unbiased","title":"Unsupervised Learning with Stein's Unbiased Risk Estimator","date":"2018-05-26","arxiv_id":"1805.10531","repositories_listed":1,"syntology":null},{"url":"/paper/compressed-sensing-of-scanning-transmission","slug":"compressed-sensing-of-scanning-transmission","title":"Compressed Sensing of Scanning Transmission Electron Microscopy (STEM) on Non-Rectangular Scans","date":"2018-05-13","arxiv_id":"1805.04957","repositories_listed":1,"syntology":null},{"url":"/paper/k-space-deep-learning-for-accelerated-mri","slug":"k-space-deep-learning-for-accelerated-mri","title":"k-Space Deep Learning for Accelerated MRI","date":"2018-05-10","arxiv_id":"1805.03779","repositories_listed":1,"syntology":null},{"url":"/paper/an-efficient-deep-convolutional-laplacian","slug":"an-efficient-deep-convolutional-laplacian","title":"An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios","date":"2018-04-13","arxiv_id":"1804.04970","repositories_listed":1,"syntology":null},{"url":"/paper/copula-variational-bayes-inference-via","slug":"copula-variational-bayes-inference-via","title":"Copula Variational Bayes inference via information geometry","date":"2018-03-29","arxiv_id":"1803.10998","repositories_listed":1,"syntology":null},{"url":"/paper/task-aware-compressed-sensing-with-generative","slug":"task-aware-compressed-sensing-with-generative","title":"Task-Aware Compressed Sensing with Generative Adversarial Networks","date":"2018-02-05","arxiv_id":"1802.01284","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-b-mode-ultrasound-image","slug":"efficient-b-mode-ultrasound-image","title":"Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning","date":"2017-12-17","arxiv_id":"1712.06096","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-multi-penalty-regularization-based","slug":"adaptive-multi-penalty-regularization-based","title":"Adaptive multi-penalty regularization based on a generalized Lasso path","date":"2017-10-11","arxiv_id":"1710.03971","repositories_listed":1,"syntology":null},{"url":"/paper/general-phase-regularized-reconstruction","slug":"general-phase-regularized-reconstruction","title":"General Phase Regularized Reconstruction using Phase Cycling","date":"2017-09-15","arxiv_id":"1709.05374","repositories_listed":1,"syntology":null},{"url":"/paper/compressed-sensing-mri-reconstruction-using-a","slug":"compressed-sensing-mri-reconstruction-using-a","title":"Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss","date":"2017-09-03","arxiv_id":"1709.00753","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-sparse-ternary-projections-for","slug":"deep-learning-sparse-ternary-projections-for","title":"Deep Learning Sparse Ternary Projections for Compressed Sensing of Images","date":"2017-08-28","arxiv_id":"1708.08311","repositories_listed":1,"syntology":null},{"url":"/paper/optimal-errors-and-phase-transitions-in-high","slug":"optimal-errors-and-phase-transitions-in-high","title":"Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models","date":"2017-08-10","arxiv_id":"1708.03395","repositories_listed":1,"syntology":null},{"url":"/paper/motion-compensated-dynamic-mri-reconstruction","slug":"motion-compensated-dynamic-mri-reconstruction","title":"Motion Compensated Dynamic MRI Reconstruction with Local Affine Optical Flow Estimation","date":"2017-07-22","arxiv_id":"1707.07089","repositories_listed":1,"syntology":null},{"url":"/paper/hyperparameter-optimization-a-spectral","slug":"hyperparameter-optimization-a-spectral","title":"Hyperparameter Optimization: A Spectral Approach","date":"2017-06-02","arxiv_id":"1706.00764","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hyperparameter-optimization-a-spectral#ran","syntology_url":"https://syntology.ai/paper/1706.00764","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.00764"}},"official":{"repos":["callowbird/Harmonica"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-with-domain-adaptation-for","slug":"deep-learning-with-domain-adaptation-for","title":"Deep Learning with Domain Adaptation for Accelerated Projection-Reconstruction MR","date":"2017-03-03","arxiv_id":"1703.01135","repositories_listed":1,"syntology":null},{"url":"/paper/horseshoe-regularization-for-feature-subset","slug":"horseshoe-regularization-for-feature-subset","title":"Horseshoe Regularization for Feature Subset Selection","date":"2017-02-23","arxiv_id":"1702.07400","repositories_listed":1,"syntology":null},{"url":"/paper/incorporation-of-prior-knowledge-of-the","slug":"incorporation-of-prior-knowledge-of-the","title":"Incorporation of prior knowledge of the signal behavior into the reconstruction to accelerate the acquisition of MR diffusion data","date":"2017-02-09","arxiv_id":"1702.02743","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-learning-via-sparse-label","slug":"semi-supervised-learning-via-sparse-label","title":"Semi-Supervised Learning via Sparse Label Propagation","date":"2016-12-05","arxiv_id":"1612.01414","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-learning-approach-to-block-based","slug":"a-deep-learning-approach-to-block-based","title":"A Deep Learning Approach to Block-based Compressed Sensing of Images","date":"2016-06-05","arxiv_id":"1606.01519","repositories_listed":1,"syntology":null},{"url":"/paper/irls-and-slime-mold-equivalence-and","slug":"irls-and-slime-mold-equivalence-and","title":"IRLS and Slime Mold: Equivalence and Convergence","date":"2016-01-12","arxiv_id":"1601.02712","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-sum-of-outer-products-dictionary-1","slug":"efficient-sum-of-outer-products-dictionary-1","title":"Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems","date":"2015-11-19","arxiv_id":"1511.06333","repositories_listed":1,"syntology":null},{"url":"/paper/statistical-physics-of-inference-thresholds","slug":"statistical-physics-of-inference-thresholds","title":"Statistical physics of inference: Thresholds and algorithms","date":"2015-11-08","arxiv_id":"1511.02476","repositories_listed":1,"syntology":null},{"url":"/paper/robust-estimation-of-self-exciting","slug":"robust-estimation-of-self-exciting","title":"Robust Estimation of Self-Exciting Generalized Linear Models with Application to Neuronal Modeling","date":"2015-07-14","arxiv_id":"1507.03955","repositories_listed":1,"syntology":null},{"url":"/paper/compressed-sensing-with-side-information","slug":"compressed-sensing-with-side-information","title":"Compressed Sensing With Side Information: Geometrical Interpretation and Performance Bounds","date":"2014-10-10","arxiv_id":"1410.2724","repositories_listed":1,"syntology":null},{"url":"/paper/the-stone-transform-multi-resolution-image","slug":"the-stone-transform-multi-resolution-image","title":"The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video","date":"2013-11-14","arxiv_id":"1311.3405","repositories_listed":1,"syntology":null},{"url":"/paper/metrics-for-multivariate-dictionaries","slug":"metrics-for-multivariate-dictionaries","title":"Metrics for Multivariate Dictionaries","date":"2013-02-18","arxiv_id":"1302.4242","repositories_listed":1,"syntology":null},{"url":null,"slug":"cross-channel-unlabeled-sensing-over-a-union","title":"Cross-Channel Unlabeled Sensing over a Union of Signal Subspaces","date":"2025-06-11","arxiv_id":"2506.09773","repositories_listed":0,"syntology":null},{"url":null,"slug":"near-field-directional-modulation-for-ris","title":"Near-Field Directional Modulation for RIS-Aided Movable Antenna MIMO Systems with Hardware Impairments","date":"2025-06-01","arxiv_id":"2506.00972","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-network-aided-detection-for-the","title":"Graph Neural Network Aided Detection for the Multi-User Multi-Dimensional Index Modulated Uplink","date":"2025-05-27","arxiv_id":"2505.21343","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-discreteness-finite-sample-analysis-of","title":"Beyond Discreteness: Finite-Sample Analysis of Straight-Through Estimator for Quantization","date":"2025-05-23","arxiv_id":"2505.18113","repositories_listed":0,"syntology":null},{"url":null,"slug":"simplicity-is-key-an-unsupervised-pretraining","title":"Simplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels","date":"2025-05-19","arxiv_id":"2505.13055","repositories_listed":0,"syntology":null},{"url":null,"slug":"near-field-channel-estimation-for-xl-mimo-a","title":"Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information","date":"2025-05-11","arxiv_id":"2505.06900","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-vector-compressed-sensing-using-james","title":"Optimal Vector Compressed Sensing Using James Stein Shrinkage","date":"2025-05-01","arxiv_id":"2505.00326","repositories_listed":0,"syntology":null},{"url":null,"slug":"parametrized-stacked-intelligent-metasurfaces","title":"Parametrized Stacked Intelligent Metasurfaces for Bistatic Integrated Sensing and Communications","date":"2025-04-29","arxiv_id":"2504.20661","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-probabilistic-models-for","title":"Diffusion Probabilistic Models for Compressive SAR Imaging","date":"2025-04-23","arxiv_id":"2504.17053","repositories_listed":0,"syntology":null},{"url":null,"slug":"modular-xl-array-enabled-3-d-localization","title":"Modular XL-Array-Enabled 3-D Localization based on Hybrid Spherical-Planar Wave Model in Terahertz Systems","date":"2025-04-18","arxiv_id":"2504.13455","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-convergence-detector-based-on-vamp","title":"Parameter Convergence Detector Based on VAMP Deep Unfolding: A Novel Radar Constant False Alarm Rate Detection Algorithm","date":"2025-04-14","arxiv_id":"2504.09912","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-guarantees-for-optimized-sampling","title":"Denoising guarantees for optimized sampling schemes in compressed sensing","date":"2025-04-01","arxiv_id":"2504.01046","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-architectures-for-the-learning-of","title":"Universal Architectures for the Learning of Polyhedral Norms and Convex Regularizers","date":"2025-03-24","arxiv_id":"2503.19190","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-beamforming-and-compressed-sensing-for","title":"Joint Beamforming and Compressed Sensing for Uplink Grant-Free Access","date":"2025-03-09","arxiv_id":"2503.06793","repositories_listed":0,"syntology":null},{"url":null,"slug":"pulse-processing-overview-and-challenges","title":"Pulse Processing -- Overview and Challenges","date":"2025-03-09","arxiv_id":"2503.06408","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerated-patient-specific-non-cartesian","title":"Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations","date":"2025-03-07","arxiv_id":"2503.05051","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-superposition-to-sparse-codes","title":"From superposition to sparse codes: interpretable representations in neural networks","date":"2025-03-03","arxiv_id":"2503.01824","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-sparsity-methods-for-compressive-space","title":"Group Sparsity Methods for Compressive Space-Frequency Channel Estimation and Spatial Equalization in Fluid Antenna System","date":"2025-03-03","arxiv_id":"2503.02004","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-worst-case-dimensionality-reduction","title":"Beyond Worst-Case Dimensionality Reduction for Sparse Vectors","date":"2025-02-27","arxiv_id":"2502.19865","repositories_listed":0,"syntology":null},{"url":null,"slug":"t1-pilot-optimized-trajectories-for-t1","title":"T1-PILOT: Optimized Trajectories for T1 Mapping Acceleration","date":"2025-02-27","arxiv_id":"2502.20333","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-accurate-manifold-denoising-by-tunneling","title":"Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization","date":"2025-02-24","arxiv_id":"2502.16819","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-projections-and-natural-sparsity-in","title":"Random Projections and Natural Sparsity in Time-Series Classification: A Theoretical Analysis","date":"2025-02-24","arxiv_id":"2502.17061","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-accelerated-mri-reconstruction","title":"Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching","date":"2025-02-24","arxiv_id":"2502.17174","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-inverse-problems-with-deep-linear","title":"Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay","date":"2025-02-21","arxiv_id":"2502.15522","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-bit-compressed-sensing-using-generative","title":"One-bit Compressed Sensing using Generative Models","date":"2025-02-18","arxiv_id":"2502.12762","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-warm-start-your-unfolding-network","title":"How to warm-start your unfolding network","date":"2025-02-03","arxiv_id":"2502.01854","repositories_listed":0,"syntology":null},{"url":null,"slug":"fuzzylight-a-robust-two-stage-fuzzy-approach","title":"FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities","date":"2025-01-27","arxiv_id":"2501.15820","repositories_listed":0,"syntology":null},{"url":null,"slug":"snapshot-compressed-imaging-based-single","title":"Snapshot Compressed Imaging Based Single-Measurement Computer Vision for Videos","date":"2025-01-25","arxiv_id":"2501.15122","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-confocal-laser-scanning-microscopy","title":"Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders","date":"2025-01-24","arxiv_id":"2501.14709","repositories_listed":0,"syntology":null},{"url":null,"slug":"keypoint-detection-empowered-near-field-user","title":"Keypoint Detection Empowered Near-Field User Localization and Channel Reconstruction","date":"2025-01-21","arxiv_id":"2501.11844","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-complexity-detection-of-multiple","title":"Low-Complexity Detection of Multiple Preambles in the Presence of Mobility and Delay Spread","date":"2025-01-10","arxiv_id":"2501.06355","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-sylvester-posterior-inference-for","title":"Deep Sylvester Posterior Inference for Adaptive Compressed Sensing in Ultrasound Imaging","date":"2025-01-07","arxiv_id":"2501.03825","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-self-supervised-diffusion-bridge-for-mri","title":"A Self-supervised Diffusion Bridge for MRI Reconstruction","date":"2025-01-06","arxiv_id":"2501.03430","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-convex-tensor-recovery-from-local","title":"Non-Convex Tensor Recovery from Local Measurements","date":"2024-12-23","arxiv_id":"2412.17281","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-beam-alignment-in-sub-thz-d2d","title":"Distributed Beam Alignment in sub-THz D2D Networks","date":"2024-12-20","arxiv_id":"2412.16015","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-sensing-based-residual-recovery","title":"Compressed Sensing Based Residual Recovery Algorithms and Hardware for Modulo Sampling","date":"2024-12-17","arxiv_id":"2412.12724","repositories_listed":0,"syntology":null},{"url":null,"slug":"rapid-reconstruction-of-extremely-accelerated","title":"Rapid Reconstruction of Extremely Accelerated Liver 4D MRI via Chained Iterative Refinement","date":"2024-12-14","arxiv_id":"2412.10629","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-sample-generation-of-diffusion","title":"Enhancing Sample Generation of Diffusion Models using Noise Level Correction","date":"2024-12-07","arxiv_id":"2412.05488","repositories_listed":0,"syntology":null},{"url":null,"slug":"delay-doppler-signal-processing-with-zadoff","title":"Delay-Doppler Signal Processing with Zadoff-Chu Sequences","date":"2024-12-05","arxiv_id":"2412.04295","repositories_listed":0,"syntology":null},{"url":null,"slug":"windowed-dictionary-design-for-delay-aware","title":"Windowed Dictionary Design for Delay-Aware OMP Channel Estimation under Fractional Doppler","date":"2024-12-02","arxiv_id":"2412.01498","repositories_listed":0,"syntology":null},{"url":null,"slug":"wtdun-wavelet-tree-structured-sampling-and","title":"WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing","date":"2024-11-25","arxiv_id":"2411.16336","repositories_listed":0,"syntology":null},{"url":null,"slug":"compute-optimal-inference-and-provable","title":"Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders","date":"2024-11-20","arxiv_id":"2411.13117","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-signal-space-to-stp-cs","title":"From Signal Space To STP-CS","date":"2024-11-20","arxiv_id":"2411.12999","repositories_listed":0,"syntology":null},{"url":"/paper/training-physics-driven-deep-learning","slug":"training-physics-driven-deep-learning","title":"Fast MRI for All: Bridging Equity Gaps via Training without Raw Data Access","date":"2024-11-20","arxiv_id":"2411.13022","repositories_listed":0,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":3,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":11,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/training-physics-driven-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2411.13022","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.13022"}},"official":null}},{"url":null,"slug":"low-rank-conjugate-gradient-net-for","title":"Low-Rank Conjugate Gradient-Net for Accelerated Cardiac MR Imaging","date":"2024-11-17","arxiv_id":"2411.11175","repositories_listed":0,"syntology":null},{"url":null,"slug":"yoso-you-only-sample-once-via-compressed","title":"YOSO: You-Only-Sample-Once via Compressed Sensing for Graph Neural Network Training","date":"2024-11-08","arxiv_id":"2411.05693","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-spectrum-sensing-with-1-bit-adcs","title":"Compressive Spectrum Sensing with 1-bit ADCs","date":"2024-11-07","arxiv_id":"2411.04611","repositories_listed":0,"syntology":null},{"url":null,"slug":"newtonized-orthogonal-matching-pursuit-for","title":"Newtonized Orthogonal Matching Pursuit for High-Resolution Target Detection in Sparse OFDM ISAC Systems","date":"2024-11-05","arxiv_id":"2411.03191","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-plug-and-play-methods-for-highly","title":"Robust plug-and-play methods for highly accelerated non-Cartesian MRI reconstruction","date":"2024-11-04","arxiv_id":"2411.01955","repositories_listed":0,"syntology":null},{"url":null,"slug":"signal-processing-via-cross-dimensional","title":"Signal Processing via Cross-Dimensional Projection","date":"2024-10-30","arxiv_id":"2410.22779","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-patch-denoising-diffusion","title":"Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting","date":"2024-10-29","arxiv_id":"2410.23318","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-k-body-hamiltonians-via-compressed","title":"Learning $k$-body Hamiltonians via compressed sensing","date":"2024-10-24","arxiv_id":"2410.18928","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-structured-compressed-sensing-with","title":"Learning Structured Compressed Sensing with Automatic Resource Allocation","date":"2024-10-24","arxiv_id":"2410.18954","repositories_listed":0,"syntology":null},{"url":null,"slug":"line-spectral-analysis-using-the-g-filter-an","title":"Line Spectral Analysis Using the G-Filter: An Atomic Norm Minimization Approach","date":"2024-10-16","arxiv_id":"2410.12358","repositories_listed":0,"syntology":null}],"record_sha256":"255c1c026705950c28a1ae3b759728c49a28986a99533a936652065df80b069a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}