activity
20222024
most citedA Multibranch Convolutional Neural Network for Hyperspectral Unmixing

20 citations · 39 across the 9 of their papers we have counts for

collaborators

9 papers

quant-ph2024

In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methods

Artur Miroszewski, Marco Fellous Asiani, Jakub Mielczarek +2

Quantum Machine Learning (QML) has gathered significant attention through approaches like Quantum Kernel Machines. While these methods hold considerable promise, their quantum natu…

eess.SP20242 cited

The OPS-SAT benchmark for detecting anomalies in satellite telemetry

Bogdan Ruszczak, Krzysztof Kotowski, David Evans +1

Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus aut…

cs.CV20242 cited

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…

cs.CV2023

Cloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels

Artur Miroszewski, Jakub Mielczarek, Filip Szczepanek +4

Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of pattern recognition and classification tasks. In this work, we consider extendi…

cs.LG20231 cited

On the Importance of Sign Labeling: The Hamburg Sign Language Notation System Case Study

Maria Ferlin, Sylwia Majchrowska, Marta Plantykow +4

Labeling is the cornerstone of supervised machine learning, which has been exploited in a plethora of various applications, with sign language recognition being one of them. Howeve…

cs.CV2023

Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines

Artur Miroszewski, Jakub Mielczarek, Grzegorz Czelusta +4

Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of classification tasks. In this work, we consider extending classical SVMs with q…