AIME21

Session

AIME21 12. Nov. Afternoon

12 Nov 2021, 14:00

15. Quantum Machine Learning: Prospects and challenges

Iordanis Kerenidis (CNRS Paris & QC Ware)

12/11/2021, 14:00

Track #3

Lecture

16. Models of Network Dynamics and Learning in the Hippocampus

Szabolcs Káli (KOKI, Budapest, Hungary)

12/11/2021, 14:45

Track #3

Lecture

4. Variable Projection Networks with Applications in Signal Processing

Dr Péter Kovács (Department of Numerical Analysis, Faculty of Informatics, Eötvös Loránd University)

12/11/2021, 15:10

Track #3

Lecture

Analysis of signals by means of mathematical transformations proved to be an effective method in various aspects, such as filtering, system identification, feature extraction, classification etc. The most widely used method in transform-domain techniques operates with fixed basic functions like the trigonometric functions in the Fourier transform, Walsh functions in the Walsh–Fourier...

5. AI model explainers in abstract interpretable qualitative modeling

Balázs Márk Hain (Department of Measurement and Information Systems, Budapest University of Technology and Economics), Prof. András Pataricza (Department of Measurement and Information Systems, Budapest University of Technology and Economics)

12/11/2021, 15:50

Track #3

Lecture

Our research focuses on the suitability of universal model explanatory tools and methods for qualitative abstractions of embedded AI models for V&V purposes.

The rapidly spreading solutions based on embedded artificial intelligence in cyber-physical systems have defined the behavioral model of complex systems with machine learning tools. A fundamental obstacle to their prevalence is that...

17. Industrial Challenges in Multi-task and Multi-sensor Systems

Csaba Nemes (Continental Hungary), Róbert Kabai (Continental Hungary)

12/11/2021, 16:15

Track #3

Lecture

7. Empirical System Identification and Qualitative Reasoning

András Földvári, Prof. András Pataricza (Department of Measurement and Information Systems, Budapest University of Technology and Economics)

12/11/2021, 17:00

Track #3

Lecture

In complex systems, the importance of empirical data analysis-based testing, verification, and validation increases to assure a proper level of service under the typically varying workload. Scaling of these systems needs reusable and scale-independent models for reconfigurability.
The limited faithfulness of speculative analytic models does not support complex system identification. This way,...