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Efficient Convex Relaxations for Streaming PCA

Neural Information Processing Systems

Theorem 4.2.Thefollowingholdsfor Algorithm 2: withprobabilityatleast1 , forallt T hP Pt,Ci 32 log ( 3e / ) ( C)2 t+ 1 1 , where = (C) Theempirical implementation condition allowsusCt, with specified components, 7 1: Experimentsonsyntheticdata.



Natasha 2: Faster Non-Convex Optimization Than SGD

Neural Information Processing Systems

In diverse world of deep learning research has given rise to numerous architectures for neural networks(convolutionalones,longshorttermmemoryones,etc). However,tothisdate,theunderlying training algorithms for neural networks are still stochastic gradient descent (SGD) and its heuristic variants.