Search Results for author:
Found 4 papers, 2 papers with code
Riemannian Covariance Fitting for Direction-of-Arrival Estimation
In this paper, as a remedy, we present a comprehensive study of the use of various Riemannian metrics of HPD matrices in CF.
The Expected Loss of Preconditioned Langevin Dynamics Reveals the Hessian Rank
Langevin dynamics (LD) is widely used for sampling from distributions and for optimization.
On Interference-Rejection Using Riemannian Geometry for Direction of Arrival Estimation
We consider the problem of estimating the direction of arrival of desired acoustic sources in the presence of multiple acoustic interference sources.
Option Discovery in the Absence of Rewards with Manifold Analysis
Options have been shown to be an effective tool in reinforcement learning, facilitating improved exploration and learning.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
