Training large deep neural networks on massive datasets is computationally very challenging.
There has been recent surge in interest in using large batch stochastic optimization …

Advances in the chemical synthesis of C-pyranosides/furanosides are summarized,
covering the literature from 2000 to 2016. The majority of the methods take advantage of the …

Supercapacitors can harvest electrical energy from intermittent sources and transfer it quickly,
but their specific energy must be raised if they are applied to efficiently power wearable …

Phytohormone crosstalk is crucial for plant defenses against pathogens and insects in
which salicylic acid (SA), jasmonic acid (JA), and ethylene (ET) play key roles. These low …

Abstract Domain integration by Gauss quadrature in the Galerkin mesh‐free methods adds
considerable complexity to solution procedures. Direct nodal integration, on the other hand, …

… The logic behind linear LR scaling is straight-forward: if you increase B by k while keeping
the number of epochs unchanged, you will do k fewer steps. So it seems natural to increase …

Vision and language are the two foundational senses for humans, and they build up our
cognitive ability and intelligence. While significant breakthroughs have been made in AI …

In this paper, we investigate large scale computers' capability of speeding up deep neural
networks (DNN) training. Our approach is to use large batch size, powered by the Layer-wise …

Super gas barrier thin films, fabricated with layer-by-layer assembly of polyethylenimine and
graphene oxide, exhibit significantly reduced oxygen and carbon dioxide transmission rates …

1 arXiv: 1708.03888 v1 [cs. CV] 13 Aug 2017 layers based on the norm of the weights (|| w||)
and the norm of the gradients (||∇ w||). By using LARS LR, we can scale the batch size to …