SPARS 2017


Oral Papers

1. Beyond ℓ1: Data Driven Sparse Signal Recovery using DeepInverse

Mousavi and R. Baraniuk (Rice Uni., USA)

2. From Sparse Bayesian Learning to Deep Recurrent Nets

H. He (Peking Uni.), B. Xin and D. Wipf (Microsoft Research Beijing, China)

3. Cosparse Denoising: The Importance of Being Social

C. Gaultier (INRIA, France), S. Kitic (Technicolor, France), S. Bertin (IRISA, France), R. Gribonval (INRIA, France)

4. A Matrix Factorization Approach for Learning Semidefinite-Representable Regularizers

Y. Sheng Soh and V. Chandrasekaran (Caltech, USA)

5. Analyzing Convolutional Neural Networks Through the Eyes of Sparsity

V. Papyan, Y. Romano, and M. Elad (Technion, Israel)

6. Working Locally Thinking Globally: Guarantees for Convolutional Sparse Coding

V. Papyan, J. Sulam, and M. Elad (Technion, Israel)

7. Invariant Multiscale Statistics for Inverse Problems

I. Dokmanic (Uni. of Illinois, USA), J. Bruna (New York Uni., USA), S. Mallat (ENS, France), M. de Hoop (Rice Uni., USA)

8. DOA estimation in fluctuating oceans: put your glasses on!

C. Herzet (INRIA, France), A. Dremeau (ENSTA Bretagne, France)

9. Learning Dictionaries as Sums of Kronecker Products

C. Dantas and R. Gribonval (INRIA, France), R. Lopes and M. da Costa (Uni. of Campinas, Brazil)

10. Minimax Lower Bounds for Dictionary Learning from Tensor Data

Z. Shakeri, W. Bajwa, and A. Sarwate (Rutgers Uni., USA)

11. Scalable Convex Methods for Low-Rank Matrix Problems

vA. Yurtsever (EPFL, Switzerland), Q. Tran-Dinh (Uni. of North Carolina, USA), V. Cevher (EPFL, Switzerland)

12. The Nonconvex Geometry of Low-Rank Matrix Optimizations

Q. Li, Z. Zhu, and G. Tang (Colorado School of Mines, USA)

13. On Computational and Statistical Tradeoffs in Matrix Completion with Graph Information

G. Dasarathy (Rice Uni., USA), N. Rao (Uni. of Texas at Austin, USA), R. Baraniuk (Rice Uni., USA)

14. On the Tradeoff between Convergence Speed and Reconstruction Accuracy in Inverse Problems

R. Giryes (Tel Aviv Uni., Israel), Y. Eldar (Technion, Israel), A. Bronstein (Technion, Israel), G. Sapiro (Duke Uni., USA)

15. Local Linear Convergence of Primal-Dual Splitting Methods for Low Complexity Regularization

J. Liang and J. Fadili (ENSICAEN, France), G. Peyré (CNRS, DMA, ENS, France)

16. Regularized Nonlinear Acceleration

D. Scieur and F. Bach (INRIA, ENS, France), A. d’Aspremont (INRIA, ENS, CNRS, France)

17. MultiD-AMP: match up Accuracy and Fast Computation by Dynamically Denoising Data

A. Perelli and M. Davies (Uni. of Edinburgh, UK)

18. Random Moments for Sketched Mixture Learning

N. Keriven and R. Gribonval (INRIA, France), G. Blanchard (Uni. of Potsdam, Germany), Y. Traonmilin (INRIA, France)

19. Subspace Estimation from Incomplete Observations: A Precise High-Dimensional Analysis

C. Wang (Harvard Uni., USA), Y. Eldar (Technion, Israel), Y. Lu (Harvard Uni., USA)

20. A Guaranteed Poly-Logarithmic Time Relaxation for the Line Spectral Estimation Problem

M. Ferreira Da Costa and W. Dai (Imperial College of London, UK)

21. Spikes super-resolution with random Fourier sampling

Y. Traonmilin, N. Keriven, R. Gribonval (INRIA, France) and G. Blanchard (Uni. of Potsdam, Germany)

22. Sampling the Fourier Transform along Radial Lines

C. Dossal (Uni. of Bordeaux, France), C. Poon (Uni. of Cambridge, UK), V. Duval (INRIA, France)

23. A Simple Convex Program for Phase Retrieval Using Anchor Vectors

S. Bahmani and J. Romberg (Georgia Institute of Technology, USA)

24. Low Rank Phase Retrieval

N. Vaswani and S. Nayer (Iowa State Uni., USA), and Y. Eldar (Technion, Israel)

25. Convolutional Phase Retrieval via Gradient Descent

Q. Qu and Y. Zhang (Columbia Uni., USA), Y. Eldar (Technion, Israel), J. Wright (Columbia Uni., USA)

26. PhaseMax: Convex Phase Retrieval Without Lifting

T. Goldstein (Stanford Uni., USA), C. Studer (Cornell Uni., USA)

27. Robust Outlier Identification for Noisy Data via Randomized Adaptive Compressive Sampling

X. Li and J. Haupt (Uni. of Minnesota, USA)

28. Robustness to Unknown Error in Sparse Regularization

S. Brugiapaglia and B. Adcock (Simon Fraser Uni., USA), R. Archibald (Oak Ridge National Laboratory, USA)

29. Sparse Recovery From Superimposed Non-Linear Sensor Measurements

M. Genzel and P. Jung (Technical Uni. of Berlin, Germany)

30. Recovery of Nonlinearly Degraded Sparse Signals through Rational Optimization

M. Castella (Telecom SudParis, France), J.-C. Pesquet (Uni. Paris-Saclay, France)

31. Computing the Spark of a Matrix

Andreas Tillmann (RWTH Aachen Uni., Germany), M. Pfetsch (Technical Uni. of Darmstadt, Germany)

32. Stabilizing Embedology: Geometry-Preserving Delay-Coordinate Maps

C. Rozell (Georgia Institute of Technology, USA), M. Wakin (Colorado School of Mines, USA), H. Lun Yap (Georgia Institute of Technology, USA), A. Eftekhari (The Alan Turing Institute, USA)

33. Rare Eclipses in Quantised Random Embeddings of Disjoint Convex Sets: a Matter of Consistency?

V. Cambareri, C. Xu, and L. Jacques (Université Catholique de Louvain, Belgium)

34. Multilinear Low-Rank Tensors on Graphs & Applications

N. Shahid, F. Grassi, and P. Vandergheynst (EPFL, Switzerland)

Poster Session 1, Tuesday, June 6th

35. Compressed Learning: A Deep Neural Network Approach

A. Adler, E. Zisselman, and M. Elad (Technion, Israel)

36. Identifying Archetypes by Exploiting Sparsity of Convex Representations

V. Abrol, P. Sharma, and A. Sao (IIT Mandi, India)

37. Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence

C. Deledalle and N. Papadakis (IMB Bordeaux, France), J. Salmon (TELECOM ParisTech, France), S. Vaiter (IMB Bordeaux, France)

38. Learning Fast Orthonormal Sparsifying Transforms

C. Rusu and J. Thompson (University of Edinburgh, UK)

39. Accelerating the Gradient Projection Iterative Sketch for Large Scale Constrained Least-squares

J. Tang, M. Golbabaee, and M. Davies (University of Edinburgh, UK)

40. Binary Graph-Signal Recovery from Noisy Samples

G. Eslamlou (Technical University of Vienna, Austria), N. Goertz (Vienna University of Technology, Austria)

41. Multiview Attenuation Computation and Correction

V. Debarnot (INSA, France), J. Kahn and P. Weiss (CNRS, France)

42. ℓ1-HOUDINI: A New Homotopy Method for ℓ1-Minimization

C. Brauer and D. Lorenz (TU Braunschweig, Germany), A. Tillmann (RWTH Aachen, Germany)

43. ADMM Pursuit for Manifold Regularized Sparse Coding

Y. Yankelevsky and M. Elad (Technion, Israel)

44. Sparsity Regularized Optical Interferometric Imaging

J. Birdi, A. Repetti, and Y. Wiaux (Heriot-Watt University, UK)

45. Sparse Maximin Aggregation of Neuronal Activity

S. Mogensen, A. Lund, and N. Hansen (University of Copenhagen, Denmark)

46. Cloud Dictionary: Sparse Coding and Modeling for Point Clouds

O. Litany, T. Remez, and A. Bronstein (Tel Aviv University, Israel)

47. Cover Tree Compressed Sensing

M. Golbabaee and M. Davies (University of Edinburgh, UK)

48. Structured Sparse Modelling with Hierarchical GP

D. Kuzin, O. Isupova, L. Mihaylova (University of Sheffield, UK)

49. An ODE-based Modeling of Inertial Forward-Backward Algorithms

V. Apidopoulos, J.-F. Aujol, C. Dossal (Université de Bordeaux, France)

50. Convex Optimisation for Partial Volume Estimation in Compressive Quantitative MRI

R. Duarte and Z. Chen (Heriot Watt University, UK), S. Gazzola (University of Bath, UK), I. Marshall and M.Davies (University of Edinburgh, UK), Y. Wiaux (Heriot-Watt University, UK)

51. Finite-Valued Sparse Signals

S. Keiper (University of Berlin, UK), G. Kutyniok (Technische Universitaet Berlin, Germany), G. Pfander and D. Lee (University of Marbur, Germany)

52. Infimal Convolution Type Coupling of First and Second Order Differences on Manifold-Valued Images

G. Steidl (University of Kaiserslauten, Germany)

53. On the Difficulty of Selecting Ising Models with Approximate Recovery

J. Scarlett and V. Cevher (EPFL, Switzerland)

54. Adaptive Orthogonal Basis Pursuit for Volumetric Two-Photon Microscopy

A. Charles (Princeton University, USA), A. Song, S. Koay, J. Gauthier, S. Thiberge, D. Tank, J. Pillow (Princeton Neuroscience Institute, USA)

55. An Iterative Convex Optimization Solver with Side Information for Joint-Sparse Signal Recovery

S.-W. Hu, S.-H. Hsieh, C.-S. Lu (Academia Sinica, Taiwan), G.-X. Lin (National Cheng-Kung University, Taiwan)

56. Image Generation Using a Sparsity Model

G. Vaksman and M. Elad (Technion, Israel)

57. Berhu Penalty for Matrix and Tensor Estimation

M. Pontil (Istituto Italiano di Tecnologia, Italy; University College London, UK), G. Denevi (Istituto Italiano di Tecnologia, Italy; Università degli studi di Genova, Italy), Michele Donini (Istituto Italiano di Tecnologia, Italy)

58. Sparsity and Low-Rank Amplitude Based Blind Source Separation

F. Feng (Université Paris-Sud, France), M. Kowalski (SUPELEC, CNRS, Université Paris-Sud, France)

59. Support Recovery Guarantees for Group Lasso Estimator

M. K. Elyaderani, S. Jain, J. Haupt, J. Druce, and S. Gonella (University of Minnesota, USA)

60. A Kaczmarz Method for Low Rank Matrix Recovery

H. Mansour and U. Kamilov (MERL, USA), O. Yilmaz (University of British Columbia, Canada)

61. Complex-valued Deterministic Matrices with Low Coherence based on Algebraic Geometric Codes

H. Abin and A. Amini (Sharif University of Technology, Iran)

62. Online Convex Optimization Meets Sparsity

S. Fosson and E. Magli (Politecnico di Torino, Italy), J. Matamoros and M. Gregori (Centre Tecnologic de Telecomunicacions de Catalunya, Spain)

63. Human Action Attribute Learning Using Low-Rank Representations

T. Wu, P. Gurram, R. Rao, and W. Bajwa (Rudgers University, USA)

64. A Low-Rank Approach to Off-the-Grid Sparse Deconvolution

P. Catala (DMA, ENS, France), V. Duval (INRIA Rocquencourt, France), G. Peyré (CNRS, DMA, ENS, France)

65. Audio Source Separation with Deep Neural Networks Using the Dropout Algorithm

A. Zermini, Y. Xu, and Q. Kong, M. Plumbley, W. Wang (University of Surrey, UK)

66. Patch-based Interferometric Phase Estimation via Mixture of Gaussian Density Modelling in the Complex Domain

J. Krishnan and J. Bioucas-Dias (Instituto de Telecomunicações, Instituto Superior Técnico, Portugal)

67. Sampling from Binary Measurements - On Reconstructions from Walsh Coefficients

L. Terhaar and A. Hansen (University of Cambridge, UK)

68. Metric Learning for Tracking a Disease Progress

N. Cvetkovic and T. Conrad (Freie Universitat Berlin, Germany)

69. Recovery of Sparse Heat Source Signals from Locally Differentially Private Sensor Data via Constrained ℓ1 Minimisation

A. McMillan and A. Gilbert (University of Michigan, USA)

70. Sparse Signal Recovery via Correlated Degradation Model

N. Eslahi (Tampere University of Technology, Finland), V. Ramakrishnan (Universidad de Concepcion, Chile), K. Wiik (University of Turku, Finland), A. Foi (Tampere University of Technology, Finland)

71. Alternating Group Sparsity for Image Restoration

K. Egiazarian and V. Katkovnik (Tampere University of Technology, Finland)

72. Compressed Sensing of FRI Signals using Annihilating Filter-based Low-rank Interpolation

J.-C. Ye and J. M. Kim (KAIST, Korea), K. H. Kim (EPFL, Switzerland), K. Lee (Georgia Tech, USA)

Poster Session 2, Wednesday, June 7

73. Sparse Denoising: Aggregation Versus Global Optimization

D. Carrera and G. Boracchi (Politecnico di Milano, Italy), A. Foi (Tampere University of Technology, Finland), B. Wohlberg (Los Alamos National Laboratory, USA)

74. Identifying Impedances of Walls Using First and Second Order Echoes

H. Peic Tukuljac, H. Lissek, and P. Vandergheynst (EPFL, Switzerland)

75. Reconstructing Signals from a Union of Linear Subspaces Using a Generalized CoSaMP

T. Tirer and R. Giryes (Tel Aviv University, Israel)

76. Compressive Spectrometer

C. C. Lu and H.-C. Chen (Industrial Technology Research Institute, Taiwan), H. T. Kung (Harvard University, USA)

77. Gap Safe Screening Rules for Faster Complex-valued Multi-task Group Lasso

M. Massias (Télécom ParisTech, Université Paris-Saclay, France), A. Gramfort and J.Salmon (Télécom ParisTech, France)

78. Block-GMCA: Sparse BSS in the large-scale regime

C. Kervazo and J. Bobin (CEA Saclay, France)

79. Compressed Dictionary Learning

F. Teixeira and K. Schnass (University of Innsbruck, Austria)

80. Slice Inverse Regression witrh Score Functions

D. Babichev and F. Bach (INRIA, ENS, France)

81. Theoretical Analysis of PCA for Heteroscedastic Data

D. Hong, L. Balzano, and J. Fessler (University of Michigan, USA)

82. Image Restoration via Successive Compression

Y. Dar, A. Bruckstein, and M. Elad (Technion, Israel)

83. Regularized Residual Quantization: a Multi-layer Sparse Dictionary Learning Approach

S. Ferdowsi, S. Voloshynovskiy, D. Kostadinov (University of Geneva, Switzerland)

84. Synthesis Sparse Modeling: Application to Image Compression and Image Error Concealment

A. Akbari and M. Trocan (Institut Superieur d’Electronique de Paris, France), B. Granado (Pierre et Marie Curie University, France)

85. Fusion of Sparse Reconstructions

W. Meiniel (Telecom ParisTech, Universite Paris-Saclay, France), J.-C. Christophe Olivo-Marin (Institut Pasteur, France), E. Angelini (Telecom ParisTech, Universite Paris-Saclay, France)

86. Compression of Multiple Input Streams into Recursive Neural Networks

A. Charles (Princeton University, USA), D. Yin (University of California, Berkeley, USA), C. Rozell (Georgia Institute of Technology, USA)

87. Joint Multichannel Deconvolution and Blind Source Separation

M. Jiang, J. Bobin, and J.-L. Starck (CEA Saclay, France)

88. Joint Multicontrast MRI Reconstruction

L. Weizman (Technion, Israel), J. Mota (Heriot-Watt University, UK), P. Song (University College London, UK), Y. Eldar (Technion, Israel), M. Rodrigues (University College London, UK)

89. A Compressed Sensing Approach for Ultrasound Imaging

A. Besson (EPFL, Switzerland), R. Carrillo (CSEM, Switzerland), D. Perdios and M. Arditi (EPFL, Switzerland), Y. Wiaux (Heriot-Watt University, UK), J.-P. Thiran (EPFL, Switzerland)

90. Correlation-Based Super-Resolution Imaging in Microscopy and Ultrasound

O. Dicker, O. Solomon, M. Mutzafi, A. Bar-Zion, M. Segev, Y. Eldar (Technion, Israel)

91. Atomic Norm Minimization for Modal Analysis from Compressive Measurements

S. Li, M. Wakin, and G. Tang (Colorado School of Mines, USA)

92. SNIPE for Memory-Limited PCA From Incomplete Data: From Failure to Success

A. Eftekhari (The Alan Turing Institute, UK), L. Balzano (University of Michigan, USA), M. Wakin and D. Yang (Colorado School of Mines, USA)

93. Joint Sparsity and SPID Calculation of the Stationary Wavelet Transform for Compressed Sensing Reconstruction in Parallel MRI

E. Shimron (Technion, Israel), A. Webb (Leiden University, The Netherlands), H. Azhari (Technion, Israel)

94. Robust Compressed Sensing with Side Information Based on Laplace Mixtures Models

C. Ravazzi (IEIIT, National Research Council, Italy), Enrico Magli (Politecnico di Torino, Italy)

95. Signal Separation with Magnitude Constraints : a Phase Unmixing Problem

A. Deleforge and Y. Traonmilin (INRIA Rennes, France)

96. Subspace Regularized Dynamic Time Warping for Spoken Query Detection

D. Ram (Idiap Research Institute and EPFL, Switzerland), A. Asaei (Idiap Research Institute, Switzerland), H. Bourlard (Idiap Research Institute and EPFL, Switzerland)

97. Graph-based Total Variation for Tomographic Image Reconstruction

F. Mahmood (Okinawa Institute of Science and Technology, Japan), N. Shahid (EPFL, Switzerland), U. Skoglund (Okinawa Institute of Science and Technology, Japan)

98. Learning Transforms With a Specified Condition Number

S. Mukherjee and C. S. Seelamantula (Indian Institute of Science, India)

99. Sparsity Order Estimation under Constrained Budget

M. K. Pirbalouti and M. F. Naeini (Shahed University, Iran), A. Amini (Sharif University of Technology, Iran)

100. Multi-Source Image Enhancement via Coupled Dictionary Learning

K. Fotiadou, G. Tsagkatakis, and P. Tsakalides (Foundation for Research and Technology, Greece)

101. Learning Convolutional Proximal Filters

U. Kamilov, H. Mansour, and D. Liu (MERL, USA)

102. Uniform Recovery Guarantees for Hadamard Sampling and Wavelet Reconstruction

V. Antun (University of Oslo, Norway), A. Hansen (University of Cambridge, UK), B. Adcock (Simon Fraser University, USA), Ø. Ryan (University of Oslo, Norway)

103. Sparse Parametric Estimaton of Poisson Processes

M. Moore and M. Davenport (Georgia Institute of Technology, USA)

104. NUWBS: Non-Uniform Wavelet Bandpass Sampling for Compressive RF Feature Acquisition

M. Pelissier (CEA LETI-MINATEC, France), C. Studer (Cornell University, USA)

105. Variable Splitting and Cycle Spinning for Sparse Signal Recovery

E. Sakhaee and A. Entezari (University of Florida, USA)

106. Improved Guarantees for Correlated-PCA (PCA when Data and Noise are Correlated)

N. Vaswani and H. Guo (Iowa State University, USA)

107. Reweighted ℓ1-norm Minimization with Guarantees: An Incremental Measurement Approach to Sparse Reconstruction

J. Mota (Heriot-Watt University, UK), L. Weizman (Technion, Israel), N. Deligiannis (Vrije Universiteit Brussel, Belgium), Y. Eldar (Technion, Israel), M. Rodrigues (University College London, UK)

108. Complex Domain Nonlocal Group-Wise Sparsity: Toward Wavelength Super-Resolution Phase Imaging in Coherent Optics

V. Katkovnik and K. Egiazarian (Tampere University of Technology, Finland)

Poster Session 3, Thursday, June 8

109. Convergence Results of GROUSE

D. Zhang and L. Balzano (University of Michigan, USA)

110. Low-Rank Tensor Regularization for Improved Dynamic Quantitative Magnetic Resonance Imaging

N. Kargas, S. Weingartner, N. Sidiropoulos, M. Akcakaya (University of Minnesota, USA)

111. Parsimonious Perceptual Information Index: Sparse Pronunciation Codes for Perceptual Phonetic Information Assessment

A Asaei (Idiap Research Institute, Switzerland)

112. Dynamic Filtering with Earth Mover’s Distance Regularization

A. Charles (Princeton University, USA), J. Lee, N. Bertrand, and C. Rozell (Georgia Institute of Technology, USA)

113. Class-adapted Blind Image Deblurring

M. Ljubenovic and M. Figueiredo (Instituto de Telecomunicações, Instituto Superior Técnico, Portugal)

114. Exploiting Joint Array and Spatial Sparsity for Broadband Source Localisation with Fisher Information Matrix Constraints

M. Chen and W. Wang (University of Surrey, UK)

115. Sparse Support Recovery with Non-smooth Loss Functions

K. Degraux (Université Catholique de Louvain, Belgium), G. Peyré (CNRS, Ecole Normale Supérieure, France), J. Fadili (ENSICAEN, CNRS, France), L. Jacques (Université Catholique de Louvain, Belgium)

116. Sparse Super-Resolution from Laplace Measurements

Q. Denoyelle (Université Paris-Dauphine, France), E. Soubies (EPFL, Switzerland), V. Duval (INRIA Rocquencourt, France), G. Peyré (CNRS, Ecole Normale Supérieure, France)

117. ℓ1/ℓ2 Regularized Non-Convex Low-Rank Matrix Factorization

P. Giampouras, A. Rontogiannis, and K. Koutroumbas (National Observatory of Athens, Greece)

118. An Efficient Direction-Of-Arrival Estimation Method Based on Weighted Sparse Spectrum Fitting

K. Ichige (Yokohama National University, Japan)

119. BPConvNet for Compressed Sensing Recovery in Bioimaging

K. H. Jin, M. McCann, M. Unser (EPFL, Switzerland)

120. Learning Non-Structured, Overcomplete and Sparsifying Transform

D. Kostadinov, S. Voloshynovskiy, S. Ferdowsi (University of Geneva, Switzerland)

121. Harmonic Mean Iteratively Reweighted Least Squares For Low-Rank Matrix Recovery

C. Kümmerle and J. Sigl (Technische Universität München, Germany)

122. Leveraging Union of Subspace Structure to Improve Constrained Clustering

J. Lipor and L. Balzano (University of Michigan, USA)

123. Optimization Convergence of Matching Pursuit Algorithms

F. Locatello and M. Tschannen (ETH Zurich, Switzerland), R. Khanna (University of Texas, Austin, USA), M. Jaggi (EPFL, Switzerland)

124. Stable Recovery of the Factors From a Deep Matrix Product

F. Malgouyres (Université de Toulouse, France), J. Landsberg (Texas A&M University, USA)

125. Magnetic Resonance Fingerprinting by exploiting Low Rank

G. Mazor and L. Weizman (Technion, Technion), A. Tal (Weizmann Institute of Science, Israel), Y. Eldar (Technion, Israel)

126. Sparse Estimation in Ordinary Differential Equation Systems

F. Mikkelsen and N. Hansen (University of Copenhagen, Denmark)

127. Compressive Hyperspectral Imaging using Coded Fourier Transform Interferometry

A. Moshtaghpour, V. Cambareri, L. Jacques (Universite catholique de Louvain, Belgium), P. Antoine and M. Roblin (Lambda-X SA, Belgium)

128. Generalized Approximate Message Passing for Noisy Quantized Compressed Sensing

O. Musa (Technical University of Vienna, Austria), N. Goertz (Vienna University of Technology, Austria)

129. Efficient Learning of Sparse Image Representations Using Homeostatic Regulation

V. Boutin, F. Ruffier, and L. Perrinet (Aix-Marseille Université, France)

130. Non-convex Blind Deconvolution Approach for Sparse Radio-Interferometric Imaging

A. Repetti, J. Birdi, and Y. Wiaux (Heriot-Watt University, UK)

131. Sequential Learning of Analysis Operators

M. Sandbichler and K. Schnass (University of Innsbruck, Austria)

132. Parameter Learning for Log-supermodular Distributions

T. Shpakova (INRIA, France), F. Bach (INRIA/ENS, France)

133. Sketching With Structured Matrices for Array Imaging

R. S. Srinivasa, M. Davenport, J. Romberg (Georgia Institute of Technology, USA)

134. High Dimensional Dictionary Learning and Applications

J. Sulam, M. Zibulevsky, M. Elad (Technion, Israel)

135. Image Sharpening using Scene-Adapted Priors

A. Teodoro, J. Bioucas-Dias, and M. Figueiredo (Instituto de Telecomunicações, Instituto Superior Técnico, Portugal)

136. Theoretical Limits of Streaming Inference and Mini-Batch Message-Passing Algorithms

A. Manoel (Neurospin CEA, France), E. Tramel (INRIA, France), T. Lesieur and L. Zdeborová (CNRS & CEA, France), F. Krzakala (ENS, France)

137. Class-specific Image Denoising Using Importance Sampling

M. Niknejad, J. Bioucas-Dias, and M. Figueiredo (Instituto de Telecomunicações, Instituto Superior Técnico, Portugal)

138. Network-based Sparse Modeling of Breast Invasive Carcinoma Survival Data

A. Veríssimo and E. Carrasquinha (IDMEC, Portugal), M.-F. Sagot (ERABLE, INRIA, France), A. Oliveira (INESC-ID, Instituto Superior Técnico, Portugal), S. Vinga and M. Lopes (IDMEC, Portugal)

139. Dynamic One-Bit Matrix Completion

L. Xu and M. Davenport (Georgia Institute of Technology, USA)

140. Weighted Diffusion Sparse LMP Algorithm in Non-uniform Noise Environment

M. Korki (Swinburne University of Technology, Australia), H. Zayyani (Qom University of Technology, Iran)

141. On Concentration Inequalities for Sparse Vectors

K. Zhang, A. Entezari (University of Florida, USA)

142. Hyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations

L. Zhuang (Instituto de Telecomunicações, Instituto Superior Técnico, Portugal), L. Gao and B. Zhang (Chinese Academy of Sciences), J. Bioucas-Dias (Instituto de Telecomunicações, Instituto Superior Técnico, Portugal)

143. A Sparse Tensor Decomposition with Multi-Dictionary Learning Applied to Diffusion Brain Imaging

C. Caiafa (Indiana University, USA), A. Cichocki (LABSP - RIKEN, Japan), F. Pestilli (Indiana University, USA)

144. Exploiting a Hierarchy of Brain Regions for Alzheimer’s Disease Classification

H. Barros and M. Silveira (ISR and IST, Portugal)

145. Compression-based Acquisition of Structured Signals

S. Beyg (University of Southern California, USA), S. Jalali (Nokia - Bell Labs, USA), A. Maleki (Columbia University, USA), U. Mitra (University of Southern California, USA)