6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications.
For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices.
In this paper, we study the problem of estimating the direction of arrival (DOA) using a sparsely sampled uniform linear array (ULA).
However, artificial neural networks are known to exhibit poor robustness in presence of input noise, where the sequential architecture of RNNs exacerbates the problem.
We consider the problem of classifying a map using a team of communicating robots.
When considering discrete-domain moving-average processes with non-Gaussian excitation noise, the above results allow us to evaluate the block-average RID and DRB, as well as to determine a relationship between these parameters and other existing compressibility measures.
We prove that $\Omega(s\log p)$ samples suffice to learn a sparse Gaussian directed acyclic graph (DAG) from data, where $s$ is the maximum Markov blanket size.
Decision trees and random forests are well established models that not only offer good predictive performance, but also provide rich feature importance information.
In this paper, we investigate the recovery of a sparse weight vector (parameters vector) from a set of noisy linear combinations.