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2020 – today
- 2024
[c4]Thomas Kernen, Maciek Machnikowski, Gil Shabat, Hanan Shteingart:
Optimizing the PTP Control Loop. ISPCS 2024: 1-7- 2023
- [p1]Dvir Ben Or, Michael Kolomenkin, Tanya Osokin, Gil Shabat, Hanan Shteingart:
Deep Learning Quantile Regression for Robustness, Confidence and Planning. Artificial Intelligence and Cybersecurity 2023: 279-289 - 2022
- [i12]Hanan Shteingart, Gerben Oostra, Ohad Levinkron, Naama Parush, Gil Shabat, Daniel Aronovich:
Machine Learning Prescriptive Canvas for Optimizing Business Outcomes. CoRR abs/2206.10333 (2022) - 2021
- [j4]Gil Shabat, Era Choshen, Dvir Ben Or, Nadav Carmel:
Fast and Accurate Gaussian Kernel Ridge Regression Using Matrix Decompositions for Preconditioning. SIAM J. Matrix Anal. Appl. 42(3): 1073-1095 (2021) - [c3]Dvir Ben Or, Michael Kolomenkin, Gil Shabat:
DL-DDA - Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints. CoG 2021: 1-7 - [i11]Dvir Ben Or, Michael Kolomenkin, Gil Shabat:
DL-DDA - Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints. CoRR abs/2106.03075 (2021) - [i10]Guy Wolf, Gil Shabat, Hanan Shteingart:
Positivity Validation Detection and Explainability via Zero Fraction Multi-Hypothesis Testing and Asymmetrically Pruned Decision Trees. CoRR abs/2111.04033 (2021) - 2020
- [i9]Hanan Shteingart, Eran Marom, Igor Itkin, Gil Shabat, Michael Kolomenkin, Moshe Salhov, Liran Katzir:
Majority Voting and the Condorcet's Jury Theorem. CoRR abs/2002.03153 (2020) - [i8]Dvir Ben-Or, Michael Kolomenkin, Gil Shabat:
Generalized Quantile Loss for Deep Neural Networks. CoRR abs/2012.14348 (2020)
2010 – 2019
- 2019
- [i7]Gil Shabat, Era Choshen, Dvir Ben-Or, Nadav Carmel:
Fast and Accurate Gaussian Kernel Ridge Regression Using Matrix Decompositions for Preconditioning. CoRR abs/1905.10587 (2019) - 2017
- [c2]Gil Shabat, David Segev, Amir Averbuch:
Uncovering Unknown Unknowns in Financial Services Big Data by Unsupervised Methodologies: Present and Future trends. ADF@KDD 2017: 8-19 - [i6]Yariv Aizenbud, Amir Averbuch, Gil Shabat, Guy Ziv:
Similarity Search Over Graphs Using Localized Spectral Analysis. CoRR abs/1707.03311 (2017) - 2016
- [j3]Yariv Aizenbud, Gil Shabat, Amir Averbuch:
Randomized LU decomposition using sparse projections. Comput. Math. Appl. 72(9): 2525-2534 (2016) - [j2]Amir Averbuch, Gil Shabat, Yoel Shkolnisky:
Direct Inversion of the Three-Dimensional Pseudo-polar Fourier Transform. SIAM J. Sci. Comput. 38(2) (2016) - [i5]Yariv Aizenbud, Gil Shabat, Amir Averbuch:
Randomized LU Decomposition Using Sparse Projections. CoRR abs/1601.04280 (2016) - 2015
- [j1]Gil Shabat, Yaniv Shmueli, Amit Bermanis, Amir Averbuch:
Accelerating Particle Filter Using Randomized Multiscale and Fast Multipole Type Methods. IEEE Trans. Pattern Anal. Mach. Intell. 37(7): 1396-1407 (2015) - [i4]Amir Averbuch, Gil Shabat, Yoel Shkolnisky:
Direct Inversion of the 3D Pseudo-polar Fourier Transform. CoRR abs/1507.06174 (2015) - 2013
- [i3]Gil Shabat, Yaniv Shmueli, Amir Averbuch:
Missing Entries Matrix Approximation and Completion. CoRR abs/1302.6768 (2013) - [i2]Gil Shabat, Yaniv Shmueli, Amir Averbuch:
Randomized LU Decomposition. CoRR abs/1310.7202 (2013) - 2012
- [i1]Gil Shabat:
Potentials and Limits of Super-Resolution Algorithms and Signal Reconstruction from Sparse Data. CoRR abs/1205.6154 (2012)
2000 – 2009
- 2008
- [c1]Leonid P. Yaroslavsky, Gil Shabat, Barak Fishbain, Ianir A. Ideses:
Super-resolution of turbulent video: potentials and limitations. Image Processing: Algorithms and Systems 2008: 681205
Coauthor Index
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