Opening the Black Box: predicting the trainability of deep neural
networks with reconstruction entropy
Opening the Black Box: predicting the trainability of deep neural
networks with reconstruction entropy
An important challenge in machine learning is to predict the initial conditions under which a given neural network will be trainable. We present a method for predicting the trainable regime in parameter space for deep feedforward neural networks, based on reconstructing the input from subsequent activation layers via a cascade …