Training very deep neural networks requires a lot of memory. Using the tools in this package, developed jointly by Tim Salimans and Yaroslav Bulatov, you can trade off some of this memory usage with ...

Introduction Thrombectomy has revolutionized patient outcomes in acute ischemic stroke, but rates of moderate to severe disability remain high. The Perfusion Reconnaissance of the Ischemic Stroke ...

Abstract: Deep neural networks often suffer from poor performance or even training failure due to the ill-conditioned problem, the vanishing/exploding gradient problem, and the saddle point problem.

† Department of Chemistry, Chemical Theory Center, and the Minnesota Supercomputing Institute, The University of Minnesota, Minneapolis, Minnesota 55455, United States ‡ Department of ...

Abstract: Large-scale multi-objective optimization problems (LSMOPs) pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces.