title: AutoLR: A Method for Automatic Tuning of Learning Rate
abstract: One very important hyperparameter for training deep neural networks is the
  learning rate of the optimizer. The choice of learning rate schedule determines
  the computational cost of getting close to a minima, how close you actually get
  to the minima, and most importantly the kind of local minima (wide/narrow)
  attained. The kind of minima attained has a significant impact on the
  generalization accuracy of the network. Current systems employ hand tuned
  learning rate schedules, which are painstakingly tuned for each network and
  dataset. Given that the state space of schedules is huge, finding a
  satisfactory learning rate schedule can be very time consuming. In this paper,
  we present AutoLR, a method for auto-tuning the learning rate as training
  proceeds. Our method works with any optimizer, and we demonstrate results on
  SGD, Momentum, and Adam optimizers.
  
  We extensively evaluate AutoLR on multiple datasets, models, and across
  multiple optimizers. We compare favorably against state of the art learning
  rate schedules for the given dataset and models, including for ImageNet on
  Resnet-50, Cifar-10 on Resnet-18, and SQuAD fine-tuning on BERT. For example,
  AutoLR achieves an EM score of 81.2 on SQuAD v1.1 with BERT_BASE compared to
  80.8 reported in (Devlin et al. (2018)) by just auto-tuning the learning rate
  schedule. To the best of our knowledge, this is the first automatic learning
  rate tuning scheme to achieve state of the art generalization accuracy on these
  datasets with the given models.
  
authors: Nipun Kwatra V Thejas Nikhil Iyer Ramachandran Ramjee Muthian Sivathanu
hIndex: Ramachandran Ramjee 38