1 citations · 4 across the 9 of their papers we have counts for
9 papers
Decoding Quantum LDPC Codes Using Graph Neural Networks
Vukan Ninkovic, Ognjen Kundacina, Dejan Vukobratovic +2
In this paper, we propose a novel decoding method for Quantum Low-Density Parity-Check (QLDPC) codes based on Graph Neural Networks (GNNs). Similar to the Belief Propagation (BP)-b…
Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems
Ognjen Kundacina
This PhD thesis thoroughly examines the utilization of deep learning techniques as a means to advance the algorithms employed in the monitoring and optimization of electric power s…
Overview of Deep Learning Methods for Retinal Vessel Segmentation
Gorana Gojić, Ognjen Kundačina, Dragiša Mišković +1
Methods for automated retinal vessel segmentation play an important role in the treatment and diagnosis of many eye and systemic diseases. With the fast development of deep learnin…
Non-adversarial Robustness of Deep Learning Methods for Computer Vision
Gorana Gojić, Vladimir Vincan, Ognjen Kundačina +2
Non-adversarial robustness, also known as natural robustness, is a property of deep learning models that enables them to maintain performance even when faced with distribution shif…
Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimation
Ognjen Kundacina, Gorana Gojic, Mirsad Cosovic +2
Data-driven state estimation (SE) is becoming increasingly important in modern power systems, as it allows for more efficient analysis of system behaviour using real-time measureme…
Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms
Ognjen Kundacina, Gorana Gojic, Mile Mitrovic +2
Electrical power systems are increasing in size, complexity, as well as dynamics due to the growing integration of renewable energy resources, which have sporadic power generation.…