Tel: 0547847738
Room 436
We have open positions for talented M.Sc. and Ph.D. students in areas of Deep Learning, Numerical Optimization and Biomedical Imaging Research InterestsNumerical optimization, deep learning, sparse signal representations, independent component analysis, inverse problems in medical imaging Teaching
YouTube video Neural Networks Intro to Neural Networks
 Convolutional Neural Networks in 10 minutes
 Convolutional Neural Networks in 7 min (summary)
 An easy way to compute Jacobian and gradient with forward and back propagation in a graph
 Gradient of Neural Network in matrix form: Part 1, Part 2
 Elad Hoffer, Deep Learning course, Technion, 2016 (in Hebrew): Lecture 2, Lecture 3, Lecture 4, Lecture 5
 EndtoEnd Deep Learning: Applications in Speech, by Yedid Hoshen
 Deep Learning on Graphs and Manifolds, by Michael Bronstein
Optimization Introduction to Optimization, video course
 Intro to Neural Networks
 Lecture 23: Gradient and Hessian of Multivariate Function (enhanced)
 Easy way to compute Jacobian and gradient with forward and back propagation in graph
 Gradient of Neural Network in matrix form: Part 1, Part 2
 Fixed Point Iteration
 Lecture 45: Convex sets and functions (enhanced)
 Lecture 6 (enhanced).Local and global minimum. Sufficient and necessary optimality conditions
 Bisection method for finding root and minimum of 1D function
 Golden section method of 1D minimization
 Quadratic interpolation method of 1D minimization
 Cubic interpolation method of 1D minimization
 Multidimensional optimization with line search
 Gradient descent method (steepest descent)
 Newton method for multidimensional minimization. Part 1 Part 2
 Newton and GaussNewton methods for nonlinear system of equations and least squares problem
 Conjugate gradient method
 SESOP  Sequential Subspace Optimization.
 QuasiNewton Optimization Methods (BFGS, LBFGS, etc.)
 Penalty function and Augmented Lagrangian methods 2013 (Introduction)
 Penalty Multiplier Method (Augmented Lagrangian) 1
 Penalty Multiplier Method (Augmented Lagrangian) 2: Dual Interpretation
 Lagrange Duality: Conic Programming vs Nonlinear one
 Conic Lagrange Multipliers via Gradient of Penalty Function
 SESOP  sequential subspace optimization method
Image / Signal Processing
 Denoising, deconvolution and computed tomography using total variation penalty
 Maximum likelihood blind source separation (ICA)
 IntensityModulated Radiation Therapy via Conic Programming
 Blind source separation by sparse decomposition
School Math



 My short video lessons in secondary school mathematics
Society
 Как сделать экономику устойчивой к шоку
 Зеленая энергия для Ирана вместо атомной
 Green energy for Iran instead of nuclear power
 Экологические Поселки, Роботы и Интернет
Others
 Michael Unser, Wavelets Demystified

Michael Unser, Beyond the Digital Divide
Presentations (slides)
Matlab Code
Selected PublicationsSee the latest publications at my Google Scholar page:
Book Chapters:
 M. Zibulevsky, B. A. Pearlmutter, P. Bofill, and P. Kisilev, "Blind Source Separation by Sparse Decomposition", chapter in the book: S. J. Roberts, and R.M. Everson eds., Independent Component Analysis: Principles and Practice, Cambridge, 2001. gzipped ps file , pdf file
 M. Zibulevsky,"Relative Newton and Smoothing Multiplier Optimization Methods for Blind Source Separation", chapter in the book: S. Makino, T.W. Lee and H. Sawada eds., Blind Speech Separation, Springer Series: Signals and Communication Technology XV, 2007 pdf file
 R. Gribonval and M. Zibulevsky. Sparse Component Analysis, in Pierre Comon and Christian Jutten (Editors), Handbook of Blind Source Separation: Independent Component Analysis and Applications, ELSEVIER 2010, pp.367420
Papers and Reports:
thesis_yehuda_pfeffer
 L. Dascal, M. Zibulevsky and R. Kimmel, "Signal denoising by constraining the residual to be statistically noisesimilar", Technical Report, 2008 pdf file
 Sarit Shwartz, Yoav Y. Schechner and Michael Zibulevsky (2008), Blind separation of convolutive image mixtures, To be published in Neurocomputing, Special issue on Advances in Blind Signal Processing.
 M. Elad, B. Matalon, and M. Zibulevsky, "Coordinate and Subspace Optimization Methods for Linear Least Squares with NonQuadratic Regularization", Applied and Computational Harmonic Analysis, Vol. 23, pp. 346367, November 2007. pdf file
 D. Model and M. Zibulevsky (October 2006), Learning SubjectSpecic Spatial and Temporal Filters for SingleTrial EEG Classi/cation, NeuroImage, Vol 32, Issue 4, pp 16311641 pdf file
 Model D. and Zibulevsky M. (2006), Signal Reconstruction in Sensor Arrays using Sparse Representations, Signal Processing, Vol 86, Issue 3, pp 624638 pdf file
 Shwartz S., Zibulevsky M., and Schechner Y.Y. (2005), Fast kernel entropy estimation and optimization, Signal Processing, Vol 85, pp. 10451058 pdf file
 Narkiss, G. and Zibulevsky, M. (2005). "Sequential Subspace Optimization Method for LargeScale Unconstrained Problems", Tech. Report CCIT No 559, EE Dept., Technion. pdf file
 Narkiss, G. and Zibulevsky, M. (2005). "Support Vector Machine via Sequential Subspace Optimization", Tech. Report CCIT No 557, EE Dept., Technion. pdf file
 Zibulevsky, M. (2005). "Blind Source Separation using Relative Newton Method combined with Smoothing Method of Multipliers", Tech. Report CCIT No 556, EE Dept, Technion. pdf file
 A.M. Bronstein, M.M. Bronstein, M. Zibulevsky and Y.Y.Zeevi, "Sparse ICA for blind separation of transmitted and reflected images", Intl. Journal of Imaging Science and Technology (IJIST), Vol. 15/1, pp. 8491, 2005.
 A.M. Bronstein, M.M. Bronstein, M. Zibulevsky and Y.Y.Zeevi, "Blind Deconvolution of Images using Optimal Sparse Representations", IEEE Trans. on Image Processing, 14(6):726736, June 2005. pdf file
 Alexey Polonsky, Michael Zibulevsky: MEG/EEG Source Localization Using Spatiotemporal Sparse Representations. ICA 2004: 10011008 pdf file
Thesis of Alexey Polonsky
 A. Bronstein, M. Bronstein and M. Zibulevsky (2003), "Relative optimization for blind deconvolution", IEEE Trans. on Signal Processing, to appear. pdf file
 A. Bronstein, M. Bronstein and M. Zibulevsky (2003), "Blind source separation using blockcoordinate relative Newton method" pdf file
 P. Kisilev, M. Zibulevsky, Y.Y. Zeevi (2003). "Multiscale framework for blind source separation", JMLR, in press. pdf file
 Zibulevsky, M. "Blind Source Separation with Relative Newton Method", Proceedings ICA2003, pp. 897902
 Zibulevsky, M. and Pearlmutter, B.A. (2000). "Second order blind source separation by recursive splitting of signal subspaces", Proceedings ICA2000. gzipped ps file
 Bronstein M., Bronstein A. and Zibulevsky M. (2002). ``Iterative reconstruction in diffraction tomography using nonuniform FFT'' pdf file
 Bronstein A., Bronstein M., Zibulevsky M. and Zeevi Y.Y. (2002). ``Optimal nonlinear estimation of photon coordinates in PET'' pdf file
 M. Zibulevsky (2003). "Smoothing Method of Multipliers for SumMax Problems" gzipped ps file
 Bofill P., Zibulevsky, M. (2001). Underdetermined Blind Source Separation using Sparse Representations, Signal Processing, Vol.81, No 11, pp.23532362. pdf file
 M. Zibulevsky, Y.Y. Zeevi (2002). "Extraction of a single source from multichannel data using sparse decomposition", Neurocomputing 49, pp 163173. gzipped ps file
 M. Zibulevsky, P. Kisilev, Y.Y. Zeevi, B.A. Pearlmutter (2000). "Blind source separation via multinode sparse representation", NIPS2001, gzipped ps file
 Levkovitz R., Falikman D., Zibulevsky M., BenTal A., Nemirovski A. (2001) ``The design and implementation of COSEM, an iterative algorithm for fully 3D listmode data'', IEEE Trans. Med. Imaging, v. 20, #7, pp. 633642 pdf file
 Zibulevsky, M. and Pearlmutter, B.A. (2000). "Recovering shape and distribution of delays of repetitive responses in strong noise" Technical Report CS001, Computer Science Department, University of New Mexico. gzipped ps file
 Akaysha C. Tang, Barak A. Pearlmutter, and Michael Zibulevsky. Blind source separation of neuromagnetic responses. Computational Neuroscience 1999, proceedings published in Neurocomputing. In press.
 Mosheyev, L. and Zibulevsky, M. (2000). "Penalty/Barrier Multiplier Algorithm for Semidefinite Programming", Optimization Methods and Software, vol.13, No 4, pp. 235261. pdf file , gzipped dvi file
 Zibulevsky, M. (1998). "Pattern Recognition via Support Vector Machine with Computationally Efficient Nonlinear Transform ". gzipped ps
 Zibulevsky, M. (1998). "ML Reconstruction of Dynamic Pet Images from Projections and Clist ", Technical Report CS983, Computer Science Department, University of New Mexico. gzip ps
 BenTal, A. and Zibulevsky, M. (1997). ``Penalty/Barrier Multiplier Methods for Convex Programming Problems",SIAM Journal on Optimization v. 7 # 2, pp. 347366, gzip ps
 Zibulevsky M. (1996) Penalty/Barrier Multiplier Methods for LargeScale Nonlinear and Semidefinite Programming. Ph.D. Thesis. gzip ps , gzip dvi
 Kochvara, M., Zibulevsky, M. and Zowe, J. (1996). "Mechanical Design Problems with Unilateral Contact", MAN  Mathematical Modeling and Numerical Analysis, v.32, no 3, pp. 255282
 Akaysha C. Tang, Barak A. Pearlmutter, Michael Zibulevsky, Tim A. Hely, and Michael Weisend. An MEG study of response latency and variability in the human visual system during a visualmotor integration task. In Advances in Neural Information Processing Systems*99. Morgan Kaufmann, 2000, to appear.
 Greig, D., Siegelman, H. and Zibulevsky, M., (1996). "A New Class of Neural Network Activation Functions That Don't Saturate". Report. gzipped ps file
 Mosheyev, L. and Zibulevsky, M. (1996). "Penalty/Barrier Multiplier Algorithm for Semidefinite Programming: Dual Bounds and Implementation". Research Report #1/96} , Optimization Laboratory.
 Zibulevsky M. (1995) "New Penalty/Barrier and Lagrange Multiplier Approach for Semidefinite Programming". Research Report #5/95, Optimization Laboratory:
 A. BenTal, I. Yuzefovich, and M. Zibulevsky (1992). "Penalty/barrier multiplier methods for minimax and constrained smooth convex problems". Research Report 9/92, Optimization Laboratory, Faculty of Industrial Engineering and Management, Technion, Haifa, Israel.
 Kuprienko, A. and Zibulevsky, M. (1987). " Efficient Data Transmission over Highnoised Telephone Channels", NIIASS, Kiev, USSR.
Pointers to Other Pages
AMPL: A Modeling Language for Optimization
ICA  Independent Component Analysis
Some Bibliography on ICA and sparse decomposition
ICA MATLAB ASSIGNMENT
www.mathtools.net  Scientific computing links for MATLAB, C/C+, Fortran and others
NetLib: free numerical libraries
Wavelet Digest
CONNECTIONISTS: neural computation mailing list
Book: Convex Optimization by Boyd and Vandenberghe
Slides to the book book Convex Optimization by Boyd and Vandenberghe
ICA pagepapers,code,demo,links by Paris Smaragdis at MIT
ICA (independent component analysis by Allan Barros, Site in Japan )
ICA page of SALK Computational Neuroscience Laboratory
ICA CENTRAL: web page + mailing list (by JeanFrançois Cardoso) 