English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

RMSProp and ADAM continue to be extremely popular algorithms for trainingneural nets but their theoretical convergence properties have remained unclear.Further, recent work has seemed to suggest that these algorithms have worsegeneralization properties when compared to carefully tuned stochastic gradientdescent or its momentum variants. In this work, we make progress towards adeeper understanding of ADAM and RMSProp in two ways. First, we provide proofsthat these adaptive gradient algorithms are guaranteed to reach criticality forsmooth non-convex objectives, and we give bounds on the running time.  Next we design experiments to empirically study the convergence andgeneralization properties of RMSProp and ADAM against Nesterov's AcceleratedGradient method on a variety of common autoencoder setups and on VGG-9 withCIFAR-10. Through these experiments we demonstrate the interesting sensitivitythat ADAM has to its momentum parameter $\beta_1$. We show that at very highvalues of the momentum parameter ($\beta_1 = 0.99$) ADAM outperforms acarefully tuned NAG on most of our experiments, in terms of getting lowertraining and test losses. On the other hand, NAG can sometimes do better whenADAM's $\beta_1$ is set to the most commonly used value: $\beta_1 = 0.9$,indicating the importance of tuning the hyperparameters of ADAM to get bettergeneralization performance.  We also report experiments on different autoencoders to demonstrate that NAGhas better abilities in terms of reducing the gradient norms, and it alsoproduces iterates which exhibit an increasing trend for the minimum eigenvalueof the Hessian of the loss function at the iterates.

Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration