no code implementations • 27 Feb 2013 • Gil Shabat, Yaniv Shmueli, Amir Averbuch
The approximation constraint can be any whose approximated solution is known for the full matrix.
no code implementations • 1 May 2013 • Dina Dushnik, Alon Schclar, Amir Averbuch
A common approach performs background subtraction, which identifies moving objects as the portion of a video frame that differs significantly from a background model.
no code implementations • CVPR 2013 • Oren Barkan, Jonathan Weill, Amir Averbuch, Shai Dekel
One of the main challenges in Computed Tomography (CT) is how to balance between the amount of radiation the patient is exposed to during scan time and the quality of the CT image.
no code implementations • 17 Feb 2015 • Aviv Rotbart, Gil Shabat, Yaniv Shmueli, Amir Averbuch
Such approach is harder to deceive and we show that only a few file fragments from a whole file are needed for a successful classification.
no code implementations • 23 Aug 2015 • Ofir Lindenbaum, Arie Yeredor, Moshe Salhov, Amir Averbuch
The multi-view dimensionality reduction is achieved by defining a cross-view model in which an implied random walk process is restrained to hop between objects in the different views.
no code implementations • 3 Nov 2015 • Yariv Aizenbud, Amit Bermanis, Amir Averbuch
We prove that the error of the proposed algorithm is bounded.
no code implementations • 19 Nov 2015 • Moshe Salhov, Amit Bermanis, Guy Wolf, Amir Averbuch
In this paper, we present a representation framework for data analysis of datasets that is based on a closed-form decomposition of the measure-based kernel.
no code implementations • 7 Mar 2016 • Oren Barkan, Jonathan Weill, Amir Averbuch
Many of the existing methods produce a low dimensional representation that attempts to describe the intrinsic geometric structure of the original data.
no code implementations • 28 Jun 2016 • Moshe Salhov, Ofir Lindenbaum, Yariv Aizenbud, Avi Silberschatz, Yoel Shkolnisky, Amir Averbuch
Data analysis methods aim to uncover the underlying low dimensional structure imposed by the low dimensional hidden parameters by utilizing distance metrics that consider the set of attributes as a single monolithic set.
no code implementations • 12 Jul 2016 • Amit Bermanis, Aviv Rotbart, Moshe Salhov, Amir Averbuch
The dictionary enables to have a natural extension of the low-dimensional embedding to out-of-sample data points, which gives rise to a distortion-based criterion for anomaly detection.
no code implementations • 6 Jun 2017 • Ofir Lindenbaum, Yuri Bregman, Neta Rabin, Amir Averbuch
The problem of learning from seismic recordings has been studied for years.
no code implementations • 4 Jul 2017 • Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir Averbuch
We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning.
no code implementations • 11 Jul 2017 • Yariv Aizenbud, Amir Averbuch, Gil Shabat, Guy Ziv
This paper provides a new similarity detection algorithm.
no code implementations • 12 Jan 2020 • Amir Averbuch, Pekka Neittaanmaki, Valery Zheludev, Moshe Salhov, Jonathan Hauser
Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions.
no code implementations • 25 Aug 2020 • Amir Averbuch, Pekka Neittaanmaki, Valery Zheludev, Moshe Salhov, Jonathan Hauser
The combined method consists of several iterations of qWPdn and BM3D algorithms, where the output from one algorithm updates the input to the other (cross-boosting). The qWPdn and BM3D methods complement each other.
no code implementations • 28 Sep 2020 • Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger
The proposed procedure learns two non-linear transformations and simultaneously gates the input variables to identify a subset of most correlated variables.
1 code implementation • 12 Oct 2020 • Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger
We further propose $\ell_0$-Deep CCA for solving the problem of non-linear sparse CCA by modeling the correlated representations using deep nets.
no code implementations • 20 Feb 2021 • Leon Ofman, Amir Averbuch, Adi Shliselberg, Idan Benaun, David Segev, Aron Rissman
A novel artificial intelligence (AI) technique that uses machine learning (ML) methodologies combines several algorithms, which were developed by ThetaRay, Inc., is applied to NASA's Transiting Exoplanets Survey Satellite (TESS) dataset to identify exoplanetary candidates.
no code implementations • ICLR 2022 • Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger
We further propose $\ell_0$-Deep CCA for solving the problem of non-linear sparse CCA by modeling the correlated representations using deep nets.
no code implementations • 21 Feb 2022 • Bronislav Yasinnik, Moshe Salhov, Ofir Lindenbaum, Amir Averbuch
Learning from imbalanced data is one of the most significant challenges in real-world classification tasks.
no code implementations • 19 Apr 2022 • Jonathan Gradstein, Moshe Salhov, Yoav Tulpan, Ofir Lindenbaum, Amir Averbuch
When presented with a binary classification problem where the data exhibits severe class imbalance, most standard predictive methods may fail to accurately model the minority class.
no code implementations • 9 Jun 2022 • Amir Averbuch, Pekka Neittaanmäki, Valery Zheludev, Moshe Salhov, Jonathan Hauser
The paper presents an image denoising scheme by combining a method that is based on directional quasi-analytic wavelet packets (qWPs) with the state-of-the-art Weighted Nuclear Norm Minimization (WNNM) denoising algorithm.
no code implementations • 23 Jul 2023 • Guy Zamberg, Moshe Salhov, Ofir Lindenbaum, Amir Averbuch
Tables are an abundant form of data with use cases across all scientific fields.