1 code implementation • 3 Jul 2023 • Marco Pegoraro, Sanketh Vedula, Aviv A. Rosenberg, Irene Tallini, Emanuele Rodolà, Alex M. Bronstein
Quantile regression (QR) is a statistical tool for distribution-free estimation of conditional quantiles of a target variable given explanatory features.
no code implementations • 13 Apr 2023 • Eyal Rozenberg, Aviv Karnieli, Ofir Yesharim, Joshua Foley-Comer, Sivan Trajtenberg-Mills, Sarika Mishra, Shashi Prabhakar, Ravindra Pratap, Daniel Freedman, Alex M. Bronstein, Ady Arie
We propose a novel, physically-constrained and differentiable approach for the generation of D-dimensional qudit states via spontaneous parametric down-conversion (SPDC) in quantum optics.
no code implementations • 4 Oct 2022 • Barak Gahtan, Reuven Cohen, Alex M. Bronstein, Gil Kedar
The most important advantage of AARL compared to RPMA is that it is much faster and can make the necessary scheduling decisions very rapidly during every time slot, while RPMA cannot.
no code implementations • 17 Aug 2022 • Dan Navon, Alex M. Bronstein
Vision-Transformers are widely used in various vision tasks.
no code implementations • 17 Aug 2022 • Dan Navon, Alex M. Bronstein
However, the expected accuracy improvement from every additional search iteration, is still unknown.
1 code implementation • 11 Jul 2022 • Yaniv Nemcovsky, Matan Jacoby, Alex M. Bronstein, Chaim Baskin
While such perturbations are usually discussed as tailored to a specific input, a universal perturbation can be constructed to alter the model's output on a set of inputs.
1 code implementation • 30 May 2022 • Aviv A. Rosenberg, Sanketh Vedula, Yaniv Romano, Alex M. Bronstein
Despite its elegance, VQR is arguably not applicable in practice due to several limitations: (i) it assumes a linear model for the quantiles of the target $\boldsymbol{\mathrm{Y}}$ given the features $\boldsymbol{\mathrm{X}}$; (ii) its exact formulation is intractable even for modestly-sized problems in terms of target dimensions, number of regressed quantile levels, or number of features, and its relaxed dual formulation may violate the monotonicity of the estimated quantiles; (iii) no fast or scalable solvers for VQR currently exist.
1 code implementation • 10 Feb 2022 • Dean Zadok, Oren Salzman, Alon Wolf, Alex M. Bronstein
A central challenge in building robotic prostheses is the creation of a sensor-based system able to read physiological signals from the lower limb and instruct a robotic hand to perform various tasks.
1 code implementation • 11 Dec 2021 • Eyal Rozenberg, Aviv Karnieli, Ofir Yesharim, Joshua Foley-Comer, Sivan Trajtenberg-Mills, Daniel Freedman, Alex M. Bronstein, Ady Arie
In addition, our method can be readily applied for controlling other degrees of freedom of light in the SPDC process, such as the spectral and temporal properties, and may even be used in condensed-matter systems having a similar interaction Hamiltonian.
1 code implementation • 25 Mar 2021 • Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M. Bronstein, Or Litany
In this paper, we identify a "warm-up obstacle": the inability of standard warm-up stages to train high quality feature extractors and avert memorization of noisy labels.
Ranked #1 on Image Classification on CIFAR-10 (with noisy labels)
no code implementations • 20 Feb 2021 • Eyal Rozenberg, Aviv Karnieli, Ofir Yesharim, Sivan Trajtenberg-Mills, Daniel Freedman, Alex M. Bronstein, Ady Arie
We introduce a systematic approach for designing 3D nonlinear photonic crystals and pump beams for generating desired quantum correlations between structured photon-pairs.
no code implementations • 16 Dec 2020 • Nir Diamant, Tal Mund, Ohad Menashe, Aviad Zabatani, Alex M. Bronstein
Energy-saving LIDAR camera for short distances estimates an object's distance using temporally intensity-coded laser light pulses and calculates the maximum correlation with the back-scattered pulse.
1 code implementation • CVPR 2021 • Omer Dahary, Matan Jacoby, Alex M. Bronstein
Mechanical image stabilization using actuated gimbals enables capturing long-exposure shots without suffering from blur due to camera motion.
1 code implementation • 24 Aug 2020 • Evgenii Zheltonozhskii, Chaim Baskin, Alex M. Bronstein, Avi Mendelson
Unsupervised learning has always been appealing to machine learning researchers and practitioners, allowing them to avoid an expensive and complicated process of labeling the data.
Ranked #1 on Unsupervised Image Classification on ObjectNet
no code implementations • MIDL 2019 • David H. Silver, Martin Feder, Yael Gold-Zamir, Avital L. Polsky, Shahar Rosentraub, Efrat Shachor, Adi Weinberger, Pavlo Mazur, Valery D. Zukin, Alex M. Bronstein
The process of fertilizing a human egg outside the body in order to help those suffering from infertility to conceive is known as in vitro fertilization (IVF).
no code implementations • 19 Apr 2020 • Alex Karbachevsky, Chaim Baskin, Evgenii Zheltonozhskii, Yevgeny Yermolin, Freddy Gabbay, Alex M. Bronstein, Avi Mendelson
Convolutional Neural Networks (CNNs) have become common in many fields including computer vision, speech recognition, and natural language processing.
no code implementations • 4 Mar 2020 • Evgenii Zheltonozhskii, Chaim Baskin, Yaniv Nemcovsky, Brian Chmiel, Avi Mendelson, Alex M. Bronstein
Even though deep learning has shown unmatched performance on various tasks, neural networks have been shown to be vulnerable to small adversarial perturbations of the input that lead to significant performance degradation.
2 code implementations • 17 Nov 2019 • Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson
We show that with more aggressive quantization, the loss landscape becomes highly non-separable with steep curvature, making the selection of quantization parameters more challenging.
2 code implementations • 17 Nov 2019 • Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel, Maxim Fishman, Alex M. Bronstein, Avi Mendelson
In this work, we study the application of randomized smoothing as a way to improve performance on unperturbed data as well as to increase robustness to adversarial attacks.
1 code implementation • 25 Sep 2019 • Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson
Our method trains the model to achieve low-entropy feature maps, which enables efficient compression at inference time using classical transform coding methods.
no code implementations • 5 Jun 2019 • Eli Schwartz, Leonid Karlinsky, Rogerio Feris, Raja Giryes, Alex M. Bronstein
Learning from one or few visual examples is one of the key capabilities of humans since early infancy, but is still a significant challenge for modern AI systems.
Ranked #9 on Few-Shot Image Classification on Mini-ImageNet - 1-Shot Learning (using extra training data)
1 code implementation • 26 May 2019 • Brian Chmiel, Chaim Baskin, Ron Banner, Evgenii Zheltonozhskii, Yevgeny Yermolin, Alex Karbachevsky, Alex M. Bronstein, Avi Mendelson
We analyze the performance of our approach on a variety of CNN architectures and demonstrate that FPGA implementation of ResNet-18 with our approach results in a reduction of around 40% in the memory energy footprint, compared to quantized network, with negligible impact on accuracy.
2 code implementations • 22 Apr 2019 • Yochai Zur, Chaim Baskin, Evgenii Zheltonozhskii, Brian Chmiel, Itay Evron, Alex M. Bronstein, Avi Mendelson
While mainstream deep learning methods train the neural networks weights while keeping the network architecture fixed, the emerging neural architecture search (NAS) techniques make the latter also amenable to training.
2 code implementations • CVPR 2019 • Amit Alfassy, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary, Rogerio Feris, Raja Giryes, Alex M. Bronstein
We conduct numerous experiments showing promising results for the label-set manipulation capabilities of the proposed approach, both directly (using the classification and retrieval metrics), and in the context of performing data augmentation for multi-label few-shot learning.
2 code implementations • 7 Feb 2019 • Nir Diamant, Dean Zadok, Chaim Baskin, Eli Schwartz, Alex M. Bronstein
Beauty is in the eye of the beholder.
no code implementations • 27 Nov 2018 • Natan Liss, Chaim Baskin, Avi Mendelson, Alex M. Bronstein, Raja Giryes
While most works use uniform quantizers for both parameters and activations, it is not always the optimal one, and a non-uniform quantizer need to be considered.
no code implementations • 23 Aug 2018 • Sanketh Vedula, Ortal Senouf, Grigoriy Zurakhov, Alex M. Bronstein, Michael Zibulevsky, Oleg Michailovich, Dan Adam, Diana Gaitini
Frame rate is a crucial consideration in cardiac ultrasound imaging and 3D sonography.
no code implementations • 23 Aug 2018 • Ortal Senouf, Sanketh Vedula, Grigoriy Zurakhov, Alex M. Bronstein, Michael Zibulevsky, Oleg Michailovich, Dan Adam, David Blondheim
The network achieves a significant improvement in image quality for both $5-$ and $7-$line MLA resulting in a decorrelation measure similar to that of SLA while having the frame rate of MLA.
1 code implementation • 20 Aug 2018 • Tal Remez, Or Litany, Raja Giryes, Alex M. Bronstein
We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful.
1 code implementation • 12 Jun 2018 • Leonid Karlinsky, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides, Rogerio Feris, Raja Giryes, Alex M. Bronstein
Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples.
1 code implementation • NeurIPS 2018 • Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Rogerio Feris, Abhishek Kumar, Raja Giryes, Alex M. Bronstein
Our approach is based on a modified auto-encoder, denoted Delta-encoder, that learns to synthesize new samples for an unseen category just by seeing few examples from it.
no code implementations • 29 Apr 2018 • Chaim Baskin, Eli Schwartz, Evgenii Zheltonozhskii, Natan Liss, Raja Giryes, Alex M. Bronstein, Avi Mendelson
We present a novel method for neural network quantization that emulates a non-uniform $k$-quantile quantizer, which adapts to the distribution of the quantized parameters.
2 code implementations • 20 Jan 2018 • Eli Schwartz, Raja Giryes, Alex M. Bronstein
We present DeepISP, a full end-to-end deep neural model of the camera image signal processing (ISP) pipeline.
no code implementations • 17 Oct 2017 • Sanketh Vedula, Ortal Senouf, Alex M. Bronstein, Oleg V. Michailovich, Michael Zibulevsky
The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis.
1 code implementation • 11 Sep 2017 • Amit Boyarski, Alex M. Bronstein, Michael M. Bronstein
Multidimensional Scaling (MDS) is one of the most popular methods for dimensionality reduction and visualization of high dimensional data.
Computational Geometry
3 code implementations • ICCV 2017 • Or Litany, Tal Remez, Emanuele Rodolà, Alex M. Bronstein, Michael M. Bronstein
We introduce a new framework for learning dense correspondence between deformable 3D shapes.
1 code implementation • 6 Jan 2017 • Tal Remez, Or Litany, Raja Giryes, Alex M. Bronstein
We further show that a significant boost in performance of up to $0. 4$ dB PSNR can be achieved by making our network class-aware, namely, by fine-tuning it for images belonging to a specific semantic class.
2 code implementations • 6 Jan 2017 • Tal Remez, Or Litany, Raja Giryes, Alex M. Bronstein
Poisson distribution is used for modeling noise in photon-limited imaging.
no code implementations • 30 May 2016 • Raja Giryes, Yonina C. Eldar, Alex M. Bronstein, Guillermo Sapiro
Solving inverse problems with iterative algorithms is popular, especially for large data.
no code implementations • 30 Apr 2015 • Raja Giryes, Guillermo Sapiro, Alex M. Bronstein
Three important properties of a classification machinery are: (i) the system preserves the core information of the input data; (ii) the training examples convey information about unseen data; and (iii) the system is able to treat differently points from different classes.
no code implementations • 18 Dec 2014 • Raja Giryes, Guillermo Sapiro, Alex M. Bronstein
In particular, we formally prove in the longer version that DNN with random Gaussian weights perform a distance-preserving embedding of the data, with a special treatment for in-class and out-of-class data.
no code implementations • 19 Dec 2013 • Jonathan Masci, Alex M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro
In recent years, a lot of attention has been devoted to efficient nearest neighbor search by means of similarity-preserving hashing.