Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients
Abstract
Recent work has established an empirically successful framework for adapting learning rates for stochastic gradient descent (SGD). This effectively removes all needs for tuning, while automatically reducing learning rates over time on stationary problems, and permitting learning rates to grow appropriately in non-stationary tasks. Here, we extend the idea in three directions, addressing proper minibatch parallelization, including reweighted updates for sparse or orthogonal gradients, improving robustness on non-smooth loss functions, in the process replacing the diagonal Hessian estimation procedure that may not always be available by a robust finite-difference approximation. The final algorithm integrates all these components, has linear complexity and is hyper-parameter free.
Cite
@article{arxiv.1301.3764,
title = {Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients},
author = {Tom Schaul and Yann LeCun},
journal= {arXiv preprint arXiv:1301.3764},
year = {2013}
}
Comments
Published at the First International Conference on Learning Representations (ICLR-2013). Public reviews are available at http://openreview.net/document/c14f2204-fd66-4d91-bed4-153523694041#c14f2204-fd66-4d91-bed4-153523694041