12 papers
Geometric Mode-Selection Scores for Delay-Coordinates Dynamic Mode Decomposition
Yoav Harris, Hadas Benisty, Ronen Talmon
Delay-coordinates dynamic mode decomposition (DC-DMD) is widely used to extract coherent spatiotemporal modes from high-dimensional time series. A central challenge is distinguishi…
Spatial Power Estimation via Riemannian Covariance Matching
Or Cohen, Alon Amar, Ronen Talmon
We propose a new method for spatial power spectrum estimation in array processing that leverages the Riemannian geometry of Hermitian positive definite (HPD) matrices. We show that…
Complex Interpolation of Matrices with an application to Multi-Manifold Learning
Adi Arbel, Stefan Steinerberger, Ronen Talmon
Given two symmetric positive-definite matrices , we study the spectral properties of the interpolation for . The pr…
Unsupervised Machine Learning for Experimental Detection of Quantum-Many-Body Phase Transitions
Ron Ziv, David Wei, Antonio Rubio-Abadal +7
Quantum many-body (QMB) systems are generally computationally hard: the computing resources necessary to simulate them exactly can often exceed the existing computation resources b…
Sparsity-Driven Entanglement Detection in High-Dimensional Quantum States
Stav Lotan, Hugo Defienne, Ronen Talmon +1
The characterization of high-dimensional quantum entanglement is crucial for advanced quantum computing and quantum information algorithms. Traditional methods require extensive da…
Joint Hierarchical Representation Learning of Samples and Features via Informed Tree-Wasserstein Distance
Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne +1
High-dimensional data often exhibit hierarchical structures in both modes: samples and features. Yet, most existing approaches for hierarchical representation learning consider onl…