2 citations · 3 across the 5 of their papers we have counts for
6 papers
Deep Tech to Space: Space Data Centers and AI Revolution at the Edge
Jonas Weiss, Patricia Sagmeister, Gabriel Maiolini Capez +16
Dramatic cost reductions driven by private sector innovations have led to a rapid increase in the number of satellites in orbit and a corresponding surge in space-generated data. A…
Large-Scale Quantum Kernels for Hyperspectral Data Classification
A. Delilbasic, A. Miroszewski, A. Wijata +4
Quantum kernel methods have emerged as a promising approach for leveraging high-dimensional feature spaces in machine learning, particularly in domains where classical kernel metho…
On Revealing the Hidden Problem Structure in Real-World and Theoretical Problems Using Walsh Coefficient Influence
M. W. Przewozniczek, F. Chicano, R. Tinós +3
Gray-box optimization employs Walsh decomposition to obtain non-linear variable dependencies and utilize them to propose masks of variables that have a joint non-linear influence o…
Light-cone feature selection in methane hyperspectral images
Artur Miroszewski, Jakub Nalepa, Agata M. Wijata
Hyperspectral images (HSIs) capture detailed spectral information across numerous contiguous bands, enabling the extraction of intrinsic characteristics of scanned objects and area…
To center or not to center? Hyperspectral data vs. quantum covariance matrices
Artur Miroszewski, Jakub Nalepa, Agata M. Wijata
We highlight how the normalization required for embedding data in quantum states affects data centering, which can significantly influence quantum amplitude-encoded covarianc…
Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI
Vladimir Zaigrajew, Hubert Baniecki, Lukasz Tulczyjew +4
Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for ide…