👉 Fluid, in the context of computational theory and machine learning, refers to a type of neural network architecture that dynamically adjusts its internal structure during training. Unlike traditional static networks, fluid models can change their connectivity and parameters on-the-fly based on the input data and learning progress. This adaptability allows fluid networks to efficiently handle varying input complexities and can potentially lead to more efficient learning processes, reduced computational costs, and improved performance on tasks with non-stationary or evolving data distributions. The core idea behind fluid networks is to create a flexible and adaptive system that can seamlessly transition between different network configurations, optimizing performance without the need for retraining from scratch.
