2 research outputs found
Accelerated Methods for -Weakly-Quasi-Convex Problems
Many problems encountered in training neural networks are non-convex.
However, some of them satisfy conditions weaker than convexity, but which are
still sufficient to guarantee the convergence of some first-order methods. In
our work we show that some previously known first-order methods retain their
convergence rates under these weaker conditions