Task: Summarization
Input (English): We propose an efficient online hyperparameter optimization method which uses a joint dynamical system to evaluate the gradient with respect to the hyperparameters. While similar methods are usually limited to hyperparameters with a smooth impact on the model, we show how to apply it to the probability of dropout in neural networks. Finally, we show its effectiveness on two distinct tasks.
Output (English): The paper proposes a way to re-initialize y at each update of lambda and a clipping procedure of y to maintain the stability of the dynamical system.