7 citations · 19 across the 5 of their papers we have counts for
7 papers
GraphNNK -- Graph Classification and Interpretability
Zeljko Bolevic, Milos Brajovic, Isidora Stankovic +1
Graph Neural Networks (GNNs) have become a standard approach for learning from graph-structured data. However, their reliance on parametric classifiers (most often linear softmax l…
Fourier Analysis of Signals on Directed Acyclic Graphs (DAG) Using Graph Zero-Padding
Ljubisa Stankovic, Milos Dakovic, Ali Bagheri Bardi +2
Directed acyclic graphs (DAGs) are used for modeling causal relationships, dependencies, and flows in various systems. However, spectral analysis becomes impractical in this settin…
Improved Coherence Index-Based Bound in Compressive Sensing
Ljubisa Stankovic, Milos Brajovic, Danilo Mandic +2
Within the Compressive Sensing (CS) paradigm, sparse signals can be reconstructed based on a reduced set of measurements. Reliability of the solution is determined by the uniquenes…
RANSAC-Based Signal Denoising Using Compressive Sensing
Ljubisa Stankovic, Milos Brajovic, Isidora Stankovic +2
In this paper, we present an approach to the reconstruction of signals exhibiting sparsity in a transformation domain, having some heavily disturbed samples. This sparsity-driven s…
Quantization in Compressive Sensing: A Signal Processing Approach
Isidora Stankovic, Milos Brajovic, Milos Dakovic +2
Influence of the finite-length registers and quantization effects on the reconstruction of sparse and approximately sparse signals is analyzed in this paper. For the nonquantized m…
Reconstruction of Sparse and Nonsparse Signals from a Reduced Set of Samples
Ljubisa Stankovic, Isidora Stankovic
Signals sparse in a transformation domain can be recovered from a reduced set of randomly positioned samples by using compressive sensing algorithms. Simple re- construction algori…