1 research outputs found
An Elementary Approach to Convergence Guarantees of Optimization Algorithms for Deep Networks
We present an approach to obtain convergence guarantees of optimization
algorithms for deep networks based on elementary arguments and computations.
The convergence analysis revolves around the analytical and computational
structures of optimization oracles central to the implementation of deep
networks in machine learning software. We provide a systematic way to compute
estimates of the smoothness constants that govern the convergence behavior of
first-order optimization algorithms used to train deep networks. A diverse set
of example components and architectures arising in modern deep networks
intersperse the exposition to illustrate the approach.Comment: The changes from v1 to v2 include i) slightly more general results;
ii) slightly more concise proofs; iii) highway and residual networks; iv)
implicitly defined network layers; v) additional algorithm boxes and
illustration figure