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SBA Research is a research center for Information Security
funded partly by the national initiative for COMET Competence Centers for Excellent Technologies.

Tag: CORE

New Journal Article by Sebastian Raubitzek!

Sebastian Raubitzek, researcher at SBA Research, published an interesting journal article titled “Quantum-Inspired Kernel Matrices: Exploring Symmetry in Machine Learning“ in Physics Letters A via ScienceDirect by ELSEVIER. This insightful article explores how quantum principles can inspire new approaches… Read More
Portrait of man

Interesting Talk @ CGIAR

Sebastian Raubitzek and Kevin Mallinger have been invited for a special research seminar in CGIAR (Consultative Group on International Agricultural Research) about the application of complexity science in Artificial Intelligence. The talk focused on the possibility to enhance AI capacities for sustainability and productivity… Read More

This Abstract Was Written by ChatGPT: Empowering Intellectual Workers Through AI-Generated Content

With this we explore the transformative potential of AI in facilitating interdisciplinary research, enhancing learning experiences, and reducing academic workload. It delves into specific AI-driven tools that can democratize knowledge access, benefit both generalists and specialists, and streamline administrative tasks. The presentation aims to stimulate discussions on responsibly harnessing AI… Read More

On the Applicability of Quantum Machine Learning

This article shows the applicability of both classical and quantum machine learning algorithms on several data sets. Here, the authors developed one of the data sets based on quantum mechanical symmetry properties. The results show that classical machine learning algorithms still perform best in terms of accuracy and runtime, even… Read More

Combining Fractional Derivatives and Supervised Machine Learning: A Review 

A systematic review of historical and potential applications of fractional derivatives in combination with supervised machine learning. Thus, this article serves to motivate researchers dealing with data-based problems, to be specific machine learning practitioners, to adopt new tools, and enhance their existing approaches. Titel Combining Fractional Derivatives and Supervised Machine… Read More