4 papers
What Makes a Good Layer? Assessing the Layer-Wise Intrinsic Properties of Music Foundation Models
Angelos-Nikolaos Kanatas, Yuexuan Kong, Pablo Alonso-Jiménez +2
Music foundation models are commonly used as frozen audio feature extractors, yet selecting which layer to extract from remains largely heuristic. Current practice defaults to fixe…
CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning
Angelos-Nikolaos Kanatas, Charilaos Papaioannou, Alexandros Potamianos
Recent advances in music foundation models have improved audio representation learning, yet their effectiveness across diverse musical traditions remains limited. We introduce Cult…
Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems
George Stamatelis, Angelos-Nikolaos Kanatas, George C. Alexandropoulos
Multi-Agent Deep Reinforcement Learning (MADRL) has emerged as a powerful tool for optimizing decentralized decision-making systems in complex settings, such as Dynamic Spectrum Ac…
Evasive Active Hypothesis Testing with Deep Neuroevolution: The Single- and Multi-Agent Cases
George Stamatelis, Angelos-Nikolaos Kanatas, Ioannis Asprogerakas +1
Active hypothesis testing is a thoroughly studied problem that finds numerous applications in wireless communications and sensor networks. In this paper, we focus on one centralize…