👉 The supervisor fluid is a critical component in Liquid Time-constant Networks (LTCs), a type of recurrent neural network (RNN) designed for time series forecasting. It acts as a dynamic weight that adjusts based on the difference between the current output and the target, helping to stabilize the training process and improve the network's ability to learn long-term dependencies. By continuously updating this fluid value, LTCs can adaptively control the learning rate, ensuring smoother convergence and better performance on sequential data tasks. This mechanism is particularly useful in scenarios where the data exhibits non-stationary patterns or when the network needs to maintain stability during training.
