Document Type



Doctor of Philosophy


Computer Science

Date of Defense


Graduate Advisor

Sanjiv Bhatia


Badri Adhikari

Sharlee Climer

Henry Kang


Deep learning training consumes ever-increasing time and resources, and that is
due to the complexity of the model, the number of updates taken to reach good
results, and both the amount and dimensionality of the data. In this dissertation,
we will focus on making the process of training more efficient by focusing on the
step size to reduce the number of computations for parameters in each update.
We achieved our objective in two new ways: we use loss scaling as a proxy for
the learning rate, and we use learnable layer-wise optimizers. Although our work
is perhaps not the first to point to the equivalence of loss scaling and learning
rate in deep learning optimization, ours is the first to leveraging this relationship
towards more efficient training. We did not only use it in simple gradient descent,
but also we were able to extend it to other adaptive algorithms. Finally, we use
metalearning to shed light on relevant aspects, including learnable losses
and optimizers. In this regard, we developed a novel learnable optimizer and
effectively utilized it to acquire an adaptive rescaling factor and learning rate,
resulting in a significant reduction in required memory during training.