Publications

Pre-Prints

• D. Simon, J. Sulam, Y. Romano, Y. Lue, M. Elad. (2018) Improving Pursuit Algorithms Using Stochastic Resonance. arXiv preprint: 1806.10171

• J. Sulam, A. Aberdam, M. Elad, (2018). On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks. arXiv preprint:1806.00701

• A. Aberdam, J. Sulam, M. Elad, (2018). Multi Layer Sparse Coding: the Holistic Way. Submitted. arXiv preprint arXiv:1804.09788

Journal Papers

• V. Papyan, Y. Romano, J. Sulam and M. Elad, “Theoretical Foundations of Deep Learning via Sparse Representations,” in IEEE Signal Processing Magazine, vol. 35, no. 4, pp. 72-89, July 2018.

• J. Sulam, V. Papyan, Y. Romano, M. Elad, (2017). Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning.  in IEEE Transactions on Signal Processing, vol. 66, no. 15, pp. 4090-4104, Aug.1, 1 2018.

• V. Papyan*, J. Sulam*, M. Elad. Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding. IEEE Transactions on Signal Processing, 65(21), 5687-5701, 2017.  This paper was originally presented (as as pre-print) in a two-parts form. The first one focuses on the noiseless (i.e., ideal) case and it introduces the main mathematical tools employed in our work. The second part addresses the generalization of theoretical claims for approximation algorithms and noisy data.  _{*Contributed Equally.}

•  J. Sulam, M. Elad. Large Inpainting of Face Images with Trainlets. IEEE Signal Processing Letters, Is. 99, Oct. 2016. Supplementary Material. Code and Model

•  J. Sulam, Y. Romano, R. Talmon. Dynamical system classification with diffusion embedding for ECG-based person identification. Signal Processing. Vol. 130, January 2017, Pages 403–411.

• J. Sulam , B. Ophir, M. Zibulevsky and M. Elad. Trainlets: Dictionary Learning in High Dimensions. IEEE Transactions on Signal Processing, 2016, V. 64, 12, pg: 3180 – 3193. CODE AVAILABLE.

Conference Papers

• J. Sulam, V. Papyan, Y. Romano, M. Elad. Projecting onto the Multi-Layer Convolutional Sparse Coding Model. ICASSP 2018 (oral presentation @ Special Session on Learning Signal Representation using Deep Learning).

• J. Sulam, R. Ben-Ari, P. Kisilev. Maximizing AUC with Deep Learning for Classification of Imbalanced Mammogram Datasets. Eurographics Workshop on Visual Computing for Biology and Medicine 2017.

• V. Papyan, Y. Romano, J. Sulam, M. Elad. Convolutional Dictionary Learning via Local Processing. Accepted to ICCV 2017.

• J. Sulam* , Y. Romano* and M. Elad. Gaussian Mixture Diffusion. 2016 ICSEE International Conference on the Science of Electrical Engineering. Nov. 2016._{* Contributed Equally.}

• J. Turek, J. Sulam , I. Yavne and M. Elad. Fusion of Ultrasound Harmonic Imaging with Clutter Removal Using Sparse Signal Separation.
  IEEE ICASSP, Brisbane, Australia, April 19-24, 2015 (oral presentation).

• J. Sulam and M. Elad. Expected Patch Log Likelihood with a Sparse Prior. EMMCVPR, Springer, January 2015 (oral presentation).
  CODE AVAILABLE.

• J. Sulam , B. Ophir, M. Elad. Image Denoising Through Multi-Scale Learnt Dictionaries. IEEE International Conference on Image Processing (ICIP), October 27-30, 2014 (oral presentation). CODE AVAILABLE.

• J. Sulam , G. Schlotthauer, M.E. Torres. Nonlinear slight parameter changes detection: a forecasting approach. 41th Argentinean Workshop on Informatics JAIIO. August, 2012. p. 168-179, ISSN 1850-2806.