  The performance of deep neural networks is well-known to be sensitive to the
setting of their hyperparameters. Recent advances in reverse-mode automatic
differentiation allow for optimizing hyperparameters with gradients. The
standard way of computing these gradients involves a forward and backward pass
of computations. However, the backward pass usually needs to consume
unaffordable memory to store all the intermediate variables to exactly reverse
the forward training procedure. In this work we propose a simple but effective
method, DrMAD, to distill the knowledge of the forward pass into a shortcut
path, through which we approximately reverse the training trajectory.
Experiments on several image benchmark datasets show that DrMAD is at least 45
times faster and consumes 100 times less memory compared to state-of-the-art
methods for optimizing hyperparameters with minimal compromise to its
effectiveness. To the best of our knowledge, DrMAD is the first research
attempt to make it practical to automatically tune thousands of hyperparameters
of deep neural networks. The code can be downloaded from
https://github.com/bigaidream-projects/drmad
