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): Proposes an algorithm for hyperparameter optimization that can be seen as an extension of Franceschi 2017 were some estimates are warm restarted to increase the stability of the method.