117 citations · 398 across the 51 of their papers we have counts for
7 papers · 1 filter
Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy
Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne +1
Finding meaningful distances between high-dimensional data samples is an important scientific task. To this end, we propose a new tree-Wasserstein distance (TWD) for high-dimension…
Domain Adaptation for DoA Estimation in Multipath Channels with Interferences
Amitay Bar, Joseph S. Picard, Israel Cohen +1
We consider the problem of estimating the direction-of-arrival (DoA) of a desired source located in a known region of interest in the presence of interfering sources and multipath.…
On Learning what to Learn: heterogeneous observations of dynamics and establishing (possibly causal) relations among them
David W. Sroczynski, Felix Dietrich, Eleni D. Koronaki +4
Before we attempt to learn a function between two (sets of) observables of a physical process, we must first decide what the inputs and what the outputs of the desired function are…
Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters
Ya-Wei Eileen Lin, Ronen Talmon, Ron Levie
Equivariant machine learning is an approach for designing deep learning models that respect the symmetries of the problem, with the aim of reducing model complexity and improving g…
Landmark Alternating Diffusion
Sing-Yuan Yeh, Hau-Tieng Wu, Ronen Talmon +1
Alternating Diffusion (AD) is a commonly applied diffusion-based sensor fusion algorithm. While it has been successfully applied to various problems, its computational burden remai…
Riemannian Covariance Fitting for Direction-of-Arrival Estimation
Joseph S. Picard, Amitay Bar, Ronen Talmon
Covariance fitting (CF) is a comprehensive approach for direction of arrival (DoA) estimation, consolidating many common solutions. Standard practice is to use Euclidean criteria f…