514 citations · 552 across the 12 of their papers we have counts for
13 papers
On the normalized signal to noise ratio in covariance estimation
Tzvi Diskin, Ami Wiesel
We address the Normalized Signal to Noise Ratio (NSNR) metric defined in the seminal paper by Reed, Mallett and Brennan on adaptive detection. The setting is detection of a target…
Self-Supervised Learning for Covariance Estimation
Tzvi Diskin, Ami Wiesel
We consider the use of deep learning for covariance estimation. We propose to globally learn a neural network that will then be applied locally at inference time. Leveraging recent…
Probabilistic Simplex Component Analysis by Importance Sampling
Nerya Granot, Tzvi Diskin, Nicolas Dobigeon +1
In this paper we consider the problem of linear unmixing hidden random variables defined over the simplex with additive Gaussian noise, also known as probabilistic simplex componen…
CFARnet: deep learning for target detection with constant false alarm rate
Tzvi Diskin, Yiftach Beer, Uri Okun +1
We consider the problem of target detection with a constant false alarm rate (CFAR). This constraint is crucial in many practical applications and is a standard requirement in clas…
Learning to Detect with Constant False Alarm Rate
Tzvi Diskin, Uri Okun, Ami Wiesel
We consider the use of machine learning for hypothesis testing with an emphasis on target detection. Classical model-based solutions rely on comparing likelihoods. These are sensit…
Learning to Estimate Without Bias
Tzvi Diskin, Yonina C. Eldar, Ami Wiesel
The Gauss Markov theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a first s…
cofga: A Dataset for Fine Grained Classification of Objects from Aerial Imagery
Eran Dahan, Tzvi Diskin, Amit Amram +2
Detection and classification of objects in overhead images are two important and challenging problems in computer vision. Among various research areas in this domain, the task of f…
Conditional Frechet Inception Distance
Michael Soloveitchik, Tzvi Diskin, Efrat Morin +1
We consider distance functions between conditional distributions. We focus on the Wasserstein metric and its Gaussian case known as the Frechet Inception Distance (FID). We develop…
COFGA: Classification Of Fine-Grained Features In Aerial Images
Eran Dahan, Tzvi Diskin
Classification between thousands of classes in high-resolution images is one of the heavily studied problems in deep learning over the last decade. However, the challenge of fine-g…
Learning to Detect
Neev Samuel, Tzvi Diskin, Ami Wiesel
In this paper we consider Multiple-Input-Multiple-Output (MIMO) detection using deep neural networks. We introduce two different deep architectures: a standard fully connected mult…
Deep MIMO Detection
Neev Samuel, Tzvi Diskin, Ami Wiesel
In this paper, we consider the use of deep neural networks in the context of Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction to deep learning and prop…
Does high harmonic generation conserve angular momentum?
Avner Fleischer, Ofer Kfir, Tzvi Diskin +2
High harmonic generation (HHG) is a unique and useful process in which infrared or visible radiation is frequency up converted into the extreme ultraviolet and x ray spectral regio…
Quasi-phase-matching of only even-order high harmonics
Tzvi Diskin, Oren Cohen
We propose a method for generating only even-order high harmonics. We formulate a condition for a shift in the relative-phase between bi-chromatic drivers that leads to a field sig…