Publications Journal Papers

Deep Learning

R. Khatib, D. Simon, and M. Elad, Learned Greedy Method (LGM): A novel Neural Architecture for Sparse Coding and Beyond, submitted to Journal of Visual Communication and Image Representation, October 2020.
M. Elad, D. Simon, and A. Aberdam, Another Step Toward Demystifying Deep Neural Networks, PNAS Commentary, October 2020.
A. Golts, D. Freedman, and M. Elad, Unsupervised Single Image Dehazing Using Dark Channel Prior Loss, IEEE Transactions on Image Processing, Vol. 29, No. 1, Pages 2692-2701, January 2020. software
Y. Romano, A. Aberdam, J. Sulam, and M. Elad, Adversarial Noise Attacks of Deep Learning Architectures – Stability Analysis via Sparse Modeled Signals, Journal of Mathematical Imaging and Vision, Pages 1-15, 2019.
M. Scetbon, M. Elad, and P. Milanfar, Deep K-SVD Denoising, submitted to IEEE Selected Topics in Signal Processing (Special Issue on Deep Learning for Image/Video Restoration and Compression). software
A. Golts, D. Freedman, and M. Elad, Deep-Energy: Unsupervised Training of Deep Neural Networks, to appear in IEEE Transactions on Image Processing. software
J. Sulam, A. Aberdam, A. Beck, and M. Elad, On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), March 2019.
A. Aberdam, J. Sulam, and M. Elad, Multi Layer Sparse Coding: the Holistic Way, SIAM Journal on Mathematics of Data Science (SIMODS), Vol. 1, No. 1, Pages 46-77, February 2019. software
J. Sulam, V. Papyan, Y. Romano, and M. Elad, Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning, IEEE Trans. on Signal Processing, Vol. 66, No. 15, Pages 4090-4104, August 2018.
V. Papyan, Y. Romano, J. Sulam, and M. Elad, Theoretical Foundations of Deep Learning via Sparse Representations, IEEE Signal Processing Magazine, Vol. 35, No. 4, Pages 72-89, June 2018.
V. Papyan, Y. Romano, and M. Elad, Convolutional Neural Networks Analyzed via Convolutional Sparse Coding, Journal of Machine Learning Research, Vol. 18, Pages 1-52, July 2017.
D. Boublil, M. Elad, J. Shtok, and M. Zibulevsky, Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks, IEEE Transactions on Medical Imaging, Vol. 34, No. 7, Pages 1474-1485, July 2015.
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