1 citations · 2 across the 5 of their papers we have counts for
9 papers
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems
Lisha Chen, Sharu Theresa Jose, Ivana Nikoloska +3
Deep learning has achieved remarkable success in many machine learning tasks such as image classification, speech recognition, and game playing. However, these breakthroughs are of…
Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines
Ivana Nikoloska, Osvaldo Simeone
Near-term noisy intermediate-scale quantum circuits can efficiently implement implicit probabilistic models in discrete spaces, supporting distributions that are practically infeas…
Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization
Ivana Nikoloska, Osvaldo Simeone
Quantum machine learning has emerged as a potential practical application of near-term quantum devices. In this work, we study a two-layer hybrid classical-quantum classifier in wh…
Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks
Ivana Nikoloska, Osvaldo Simeone
Power control in decentralized wireless networks poses a complex stochastic optimization problem when formulated as the maximization of the average sum rate for arbitrary interfere…
Deep Reinforcement Learning-Aided Random Access
Ivana Nikoloska, Nikola Zlatanov
We consider a system model comprised of an access point (AP) and K Internet of Things (IoT) nodes that sporadically become active in order to send data to the AP. The AP is assumed…
Inference over Wireless IoT Links with Importance-Filtered Updates
Ivana Nikoloska, Josefine Holm, Anders Kalør +2
We consider a communication cell comprised of Internet-of-Things (IoT) nodes transmitting to a common Access Point (AP). The nodes in the cell are assumed to generate data samples…