Monday, June 04, 2012

Presentations: iTWIST: international - Traveling Workshop for Interacting Sparse models and Technology

  1. U. Ayaz, ``Sparse Recovery with Fusion Frames''
  2. A. Bartels, T. Alexandrov, D. Trede, ``Sparsity in Imaging Mass Spectrometry Data''
  3. P. Boufounos, ``Universal Finite-Range Embeddings''
  4. R. E. Carrillo, Y. Wiaux, ``Sparsity Averaging Reweighted Analysis (SARA)''
  5. V. Cevher, T. Hemant, ``Learning non-parametric basis independent models from point queries via low-rank methods''
  6. G. Chardon, L. Daudet, A. Cohen, ``Sampling the solutions of the Helmholtz equation''
  7. A. Jezierska, C. Chaux, J.-C. Pesquet, H. Talbot, ``A hybrid optimization approach for vector quantization''
  8. A. Mohammad-Djafari, ``Bayesian sparse sources separation''
  9. M. Ehler, C. Bachoc, ``Tight p-Fusion Frames and Robustness Against Data Loss''
  10. V. Emiya, N. Stefanakis, J. Marchal, N. Bertin, R. Gribonval, P. Cervenka, ``Underwater acoustic imaging: sparse models and implementation issues''
  11. J. Fadili, C. Deledalle, S. Vaiter, G. Peyré, C. Dossal, ``Unbiased Risk Estimation for Sparse Regularization''
  12. J.-M. Feng and C.-H. Lee, ``Subspace Pursuit-based Algorithm for Sparse Signal Recovery from Multiple Measurement Vectors''
  13. A. Fraysse and T. Rodet, ``A Bayesian algorithm for large dimensional problems involving sparse information''
  14. M. Golbabaee, P. Vandergheynst, ``Spectral Compressive Imaging via Joint Low-Rank and Sparse Recovery''
  15. A. Gonzalez, L. Jacques, P. Antoine∗, ``TV-L2 Refractive Index Map Reconstruction from Polar Domain Deflectometry''
  16. R. Gribonval, S. Nam, M. Davies, M. Elad, ``Sparsity & Co: Analysis vs. Synthesis in Low-Dimensional Signal Models"
  17. A. Gramfort, M. Kowalski, J. Haueisen, M. Hamalainen, D. Strohmeier, B. Thirion, G. Varoquaux, ``Ill-posed problems and sparsity in brain imaging: from MEG source estimation to resting state networks and supervised learning with fMRI.''
  18. M. Hügel, H. Rauhut and T. Strohmer, ``Compressed Sensing in Radar''
  19. L. Jacques, D. Hammond, J. Fadili, ``Compressed Sensing and Quantization: A Compander Approach.''
  20. M. H. Kamal, M. Golbabaee, P. Vandergheynst, ``Light Field Compressive Sensing''
  21. M. Kowalski, ``Social Sparsity and Structured Sparse Approximation''
  22. F. Krahmer, S. Mendelson, H. Rauhut, ``Compressed sensing using subsampled convolutions''
  23. I. Loris, C. Verhoeven, ``On a generalization of the iterative soft-thresholding algorithm for the case of non-separable penalty''
  24. H. Luong, B. Goossens, J. Aelterman, A. Pizurica, W. Philips, ``Color Image Restoration and Reconstruction''
  25. S. Merlet, E. Caruyer, R. Deriche, ``Accelerating Diffusion MRI via Compressive Sensing''
  26. E. Niemi, ``Total variation regularization for x-ray tomography''
  27. K. Niinimaki, ``Sparsity promoting Bayesian inversion''
  28. L. Perrinet, ``Edge statistics in "natural" images: implications for understanding lateral connectivity in V1 and improving the efficiency of sparse coding algorithms''
  29. G. Peyré, S. Vaiter, C. Dossal, J. Fadili, ``Robust Sparse Analysis Regularization''
  30. L. Ralaivola, ``On the Use of Matrix Concentration Inequalities in Machine Learning"
  31. H. Rauhut, G. Pfander and Joel Tropp, ``Sparse Recovery with Time-Frequency Structured Random Matrices''
  32. J. Romberg, A. Ahmed, W. Mantzel, K. Sabra, ``Compressed Sensing and two problems from array processing: sampling multichannel signals and localizing sources in complicated channels"
  33. D. I. Shuman, B. Ricaud, P. Vandergheynst, ``A Windowed Graph Fourier Transform''
  34. P. Sudhakar, R. Gribonval, A. Benichoux, S. Arberet, ``The use of sparsity priors for convolutive source separation''
  35. I. Tosic, S. Drewes, ``Learning joint image-depth sparse representations''
  36. I. Toumi, S. Caldarelli, B. Torrésani, ``BSS Estimation of components in DOSY NMR Spectroscopy''
  37. J. Tropp, M. McCoy, ``Sharp recovery bounds for convex deconvolution, with applications''
  38. P. Weiss, ``VSNR: variational algorithms to remove stationary noise from images''
  39. R. Willett, ``Sparsity and Scarcity in High-Dimensional Density Estimation"
  40. A. Zakharova, O. Laligant, C. Stolz, ``Depth reconstruction from defocused images using incomplete measurements''

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