| Randomized LU decomposition G Shabat, Y Shmueli, Y Aizenbud, A Averbuch Applied and Computational Harmonic Analysis 44 (2), 246-272, 2018 | 66 | 2018 |
| System, method and computer program product for detection of defects within inspection images M Dalla-Torre, G Shabat, A Dafni, A Batikoff US Patent 8,977,035, 2015 | 37 | 2015 |
| Nonuniform sampling, image recovery from sparse data and the discrete sampling theorem LP Yaroslavsky, G Shabat, BG Salomon, IA Ideses, B Fishbain Journal of the Optical Society of America A 26 (3), 566-575, 2009 | 37 | 2009 |
| Superresolution in turbulent videos: making profit from damage L Yaroslavsky, B Fishbain, G Shabat, I Ideses Optics Letters 32 (20), 3038-3040, 2007 | 32 | 2007 |
| System and method for anomaly detection in dynamically evolving data using hybrid decomposition D Segev, G Shabat, A Averbuch US Patent 10,812,515, 2020 | 29 | 2020 |
| System, method and computer program product for classification within inspection images M Dalla-Torre, G Shabat, A Dafni, A Batikoff US Patent 9,098,893, 2015 | 26 | 2015 |
| Randomized LU decomposition using sparse projections Y Aizenbud, G Shabat, A Averbuch Computers & Mathematics with Applications 72 (9), 2525-2534, 2016 | 22 | 2016 |
| Interest Zone Matrix Approximation G Shabat, A Averbuch Electronic Journal of Linear Algebra 23, 678-702, 2012 | 20 | 2012 |
| DL-DDA-Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay Constraints DB Or, M Kolomenkin, G Shabat 2021 IEEE Conference on Games (CoG), 1-7, 2021 | 18 | 2021 |
| Measure based anomaly detection A Averbuch, G Shabat, E Shabat, D Segev US Patent 9,942,254, 2018 | 16 | 2018 |
| Generalized quantile loss for deep neural networks D Ben Or, M Kolomenkin, G Shabat arXiv e-prints, arXiv: 2012.14348, 2020 | 13* | 2020 |
| Accelerating Particle Filter using Randomized Multiscale and Fast Multipole Type Methods G Shabat, Y Shmueli, A Bermanis, A Averbuch IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 | 12 | 2015 |
| Direct inversion of the three-dimensional pseudo-polar Fourier transform A Averbuch, G Shabat, Y Shkolnisky SIAM Journal on Scientific Computing 38 (2), A1100-A1120, 2016 | 11 | 2016 |
| Fast and accurate Gaussian kernel ridge regression using matrix decompositions for preconditioning G Shabat, E Choshen, DB Or, N Carmel SIAM Journal on Matrix Analysis and Applications 42 (3), 1073-1095, 2021 | 9 | 2021 |
| Superresolution in color videos acquired through turbulent media B Fishbain, IA Ideses, G Shabat, BG Salomon, LP Yaroslavsky Optics letters 34 (5), 587-589, 2009 | 8 | 2009 |
| System and method for anomaly detection in dynamically evolving data using low rank matrix decomposition A Averbuch, G Shabat, Y Shmueli US Patent 10,509,695, 2019 | 7 | 2019 |
| Uncovering unknown unknowns in financial services big data by unsupervised methodologies: Present and future trends G Shabat, D Segev, A Averbuch ACM SIGKDD 2017 Workshop on Anomaly Detection in Finance, 8-19, 2018 | 7 | 2018 |
| Super-resolution of turbulent video: potentials and limitations LP Yaroslavsky, G Shabat, B Fishbain, IA Ideses Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series …, 2008 | 6 | 2008 |
| Randomized LU Decomposition: An algorithm for dictionaries construction A Rotbart, G Shabat, Y Shmueli, A Averbuch arXiv preprint arXiv:1502.04824, 2015 | 5 | 2015 |
| Angular super-resolution retrieval in small-angle X-ray scattering B Gutman, M Mrejen, G Shabat, R Avinery, Y Shkolnisky, R Beck Scientific reports 10 (1), 16038, 2020 | 4 | 2020 |
Amir AverbuchProfessor of Computer Science, Tel-Aviv UniversityВерификована је имејл адреса на math.tau.ac.il