514 citations · 1.2k across the 31 of their papers we have counts for
34 papers
The Optimization Landscape of Carathéodory Decomposition of Toeplitz Covariances
Daniel Busbib, Ami Wiesel
Toeplitz covariance estimation is a classical signal processing problem, yet the geometry of the Gaussian maximum-likelihood objective remains only partially understood. Recent New…
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…
Learning minimal volume uncertainty ellipsoids
Itai Alon, David Arnon, Ami Wiesel
We consider the problem of learning uncertainty regions for parameter estimation problems. The regions are ellipsoids that minimize the average volumes subject to a prescribed cove…
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…