Bythe Mark zN !0asN gro waystodeter zN is "close DeterministicIfinaddition zN in (7), vN = 1 N Themisintroducedtomakethe thescaleofaysnonnegative). Ifthenthe (2) are "close " tostationarity.

This paper studies how to test the stationarity of stochastic gradient with momentum using some advanced testing statistics that take the time correlations into accounts. Extensive experiments are run to demonstrate the advantage of the proposed method over existing approaches. Originality: The paper is based on extending a recent paper by Yaida. It does not seem that original to me but the authors do combine the condition by Yaida with some more advanced testing statistics in a new way. Overall I think the extension is quite natural, so the conceptual novelty is not that high.

Artificial intelligence researchers from Facebook Inc., Princeton University and the Massachusetts Institute of Technology have teamed up to publish a new manuscript that they say offers a theoretical framework describing for the first time how deep neural networks actually work. In a blog post, Facebook AI research scientist Sho Yaida noted that DNNs are one of the key ingredients of modern AI research. But for many people, including most AI researchers, they're also considered to be too complicated to understand from first principles, he said. That's a problem, because although much progress in AI has been made through experimentation and trial and error, it means researchers are ignorant of many of the key features of DNNs that make them so incredibly useful. If researchers are more aware of these key features, it would likely lead to some dramatic advances and the development of much more capable AI models, Yaida said.