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…