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ORCIDShai Dekel
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2020 – today
- 2024
[i16]Wael Mattar, Idan Levy, Nir Sharon, Shai Dekel:
Wavelets Are All You Need for Autoregressive Image Generation. CoRR abs/2406.19997 (2024)- 2023
- [j20]Ido Ben-Shaul, Tomer Galanti, Shai Dekel:
Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions. Trans. Mach. Learn. Res. 2023 (2023) - [c5]Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti, Shai Dekel, Yann LeCun:
Reverse Engineering Self-Supervised Learning. NeurIPS 2023 - [i15]Ido Ben-Shaul, Tomer Galanti, Shai Dekel:
Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions. CoRR abs/2301.04605 (2023) - [i14]Yuval Zelig, Shai Dekel:
Numerical Methods For PDEs Over Manifolds Using Spectral Physics Informed Neural Networks. CoRR abs/2302.05322 (2023) - [i13]Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti, Shai Dekel, Yann LeCun:
Reverse Engineering Self-Supervised Learning. CoRR abs/2305.15614 (2023) - 2022
- [j19]Adar Kahana, Eli Turkel
, Shai Dekel, Dan Givoli:
A physically-informed deep-learning model using time-reversal for locating a source from sparse and highly noisy sensors data. J. Comput. Phys. 470: 111592 (2022) - [c4]Shai Dekel, Leon Gugel:
PR-DAD: Phase Retrieval Using Deep Auto-Decoders. ICFSP 2022: 165-172 - [c3]Ido Ben-Shaul, Shai Dekel:
Nearest Class-Center Simplification through Intermediate Layers. TAG-ML 2022: 37-47 - [i12]Ido Ben-Shaul, Shai Dekel:
Nearest Class-Center Simplification through Intermediate Layers. CoRR abs/2201.08924 (2022) - [i11]Leon Gugel, Shai Dekel:
PR-DAD: Phase Retrieval Using Deep Auto-Decoders. CoRR abs/2204.09051 (2022) - 2021
- [j18]Oded Ovadia, Adar Kahana, Eli Turkel, Shai Dekel:
Beyond the Courant-Friedrichs-Lewy condition: Numerical methods for the wave problem using deep learning. J. Comput. Phys. 442: 110493 (2021) - [i10]Ido Ben-Shaul, Shai Dekel:
Sparsity-Probe: Analysis tool for Deep Learning Models. CoRR abs/2105.06849 (2021) - 2020
- [j17]Adar Kahana, Eli Turkel, Shai Dekel, Dan Givoli:
Obstacle segmentation based on the wave equation and deep learning. J. Comput. Phys. 413: 109458 (2020)
2010 – 2019
- 2018
- [i9]Shai Dekel, Oren Elisha, Ohad Morgan:
Wavelet Decomposition of Gradient Boosting. CoRR abs/1805.02642 (2018) - 2017
- [j16]Oren Barkan, Jonathan Weill, Shai Dekel, Amir Averbuch:
A Mathematical Model for Adaptive Computed Tomography Sensing. IEEE Trans. Computational Imaging 3(4): 551-565 (2017) - [i8]Oren Elisha, Shai Dekel:
Function space analysis of deep learning representation layers. CoRR abs/1710.03263 (2017) - 2016
- [j15]Oren Elisha, Shai Dekel:
Wavelet decompositions of Random Forests - smoothness analysis, sparse approximation and applications. J. Mach. Learn. Res. 17: 198:1-198:38 (2016) - [j14]Tamir Bendory, Avinoam David Bar-Zion, Dan Adam, Shai Dekel, Arie Feuer:
Stable Support Recovery of Stream of Pulses With Application to Ultrasound Imaging. IEEE Trans. Signal Process. 64(14): 3750-3759 (2016) - [i7]Leonid Gugel, Yoel Shkolnisky, Shai Dekel:
Machine olfaction using time scattering of sensor multiresolution graphs. CoRR abs/1602.04358 (2016) - 2015
- [j13]Leeor Langer, Yoav Binenbaum, Leonid Gugel, Moran Amit, Ziv Gil, Shai Dekel:
Computer-aided diagnostics in digital pathology: automated evaluation of early-phase pancreatic cancer in mice. Int. J. Comput. Assist. Radiol. Surg. 10(7): 1043-1054 (2015) - [j12]Tamir Bendory, Shai Dekel, Arie Feuer:
Super-Resolution on the Sphere Using Convex Optimization. IEEE Trans. Signal Process. 63(9): 2253-2262 (2015) - [i6]Tamir Bendory, Shai Dekel, Arie Feuer:
Unified Convex Optimization Approach to Super-Resolution Based on Localized Kernels. CoRR abs/1501.01825 (2015) - [i5]Tamir Bendory, Avinoam David Bar-Zion, Dan Adam, Shai Dekel, Arie Feuer:
Stable Support Recovery of Stream of Pulses with Application to Ultrasound Imaging. CoRR abs/1507.07256 (2015) - 2014
- [j11]Tamir Bendory, Shai Dekel, Arie Feuer:
Exact recovery of non-uniform splines from the projection onto spaces of algebraic polynomials. J. Approx. Theory 182: 7-17 (2014) - [i4]Tamir Bendory, Shai Dekel, Arie Feuer:
Robust Recovery of Stream of Pulses using Convex Optimization. CoRR abs/1412.3262 (2014) - [i3]Tamir Bendory, Shai Dekel, Arie Feuer:
Super-resolution on the Sphere using Convex Optimization. CoRR abs/1412.3282 (2014) - [i2]Tamir Bendory, Shai Dekel, Arie Feuer:
Exact recovery of Dirac ensembles from the projection onto spaces of spherical harmonics. CoRR abs/1412.3284 (2014) - [i1]Tamir Bendory, Shai Dekel, Arie Feuer:
Exact recovery of non-uniform splines from the projection onto spaces of algebraic polynomials. CoRR abs/1412.6254 (2014) - 2013
- [c2]Oren Barkan, Jonathan Weill, Amir Averbuch, Shai Dekel:
Adaptive Compressed Tomography Sensing. CVPR 2013: 2195-2202 - 2012
- [j10]Shai Dekel:
On the analysis of anisotropic smoothness. J. Approx. Theory 164(8): 1143-1164 (2012) - [j9]Amir Averbuch, Shai Dekel, Shay Deutsch:
Adaptive Compressed Image Sensing Using Dictionaries. SIAM J. Imaging Sci. 5(1): 57-89 (2012) - [c1]Oren Barkan, Amir Averbuch, Shai Dekel, Yaniv Tenzer:
A Mathematical Model for Extremely Low Dose Adaptive Computed Tomography Acquisition. MMCS 2012: 13-33 - 2010
- [j8]Shai Dekel:
On the equivalence of the modulus of smoothness and the K-functional over convex domains. J. Approx. Theory 162(2): 349-362 (2010)
2000 – 2009
- 2007
- [j7]Wolfgang Dahmen, Shai Dekel, Pencho Petrushev:
Multilevel preconditioning for partition of unity methods: some analytic concepts. Numerische Mathematik 107(3): 503-532 (2007) - [j6]Dror Alani, Amir Averbuch, Shai Dekel:
Image Coding With Geometric Wavelets. IEEE Trans. Image Process. 16(1): 69-77 (2007) - [j5]Roman Kazinnik, Shai Dekel, Nira Dyn:
Low Bit-Rate Image Coding Using Adaptive Geometric Piecewise Polynomial Approximation. IEEE Trans. Image Process. 16(9): 2225-2233 (2007) - 2005
- [j4]Shai Dekel, Dany Leviatan:
Adaptive Multivariate Approximation Using Binary Space Partitions and Geometric Wavelets. SIAM J. Numer. Anal. 43(2): 707-732 (2005) - 2004
- [j3]Shai Dekel, Dany Leviatan:
On Measuring the Efficiency of Kernel Operators in Lp(Rd). Adv. Comput. Math. 20(1-3): 53-65 (2004) - [j2]Shai Dekel, Dany Leviatan:
Whitney Estimates for Convex Domains with Applications to Multivariate Piecewise Polynomial Approximation. Found. Comput. Math. 4(4): 345-368 (2004) - [j1]Shai Dekel, Dany Leviatan:
The Bramble-Hilbert Lemma for Convex Domains. SIAM J. Math. Anal. 35(5): 1203-1212 (2004)
Coauthor Index
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