Non Converging Artificial Oscillation

Non-converging artificial oscillations are a significant challenge in various computational models, particularly those involving neural networks and dynamical systems. Current research focuses on mitigating these oscillations through improved sampling techniques (e.g., hierarchical gradient-based methods), novel network architectures incorporating oscillatory dynamics (e.g., Deep Oscillatory Neural Networks, oscillation-driven reservoir computing), and refined training algorithms (e.g., methods addressing oscillations in quantization-aware training). Overcoming these oscillations is crucial for enhancing the accuracy and efficiency of machine learning models for time-series prediction, control systems, and biosignal analysis, as well as for developing more realistic and robust computational models of biological systems.