منابع مشابه
Per-Block-Convex Data Modeling by Accelerated Stochastic Approximation
Applications involving dictionary learning, non-negative matrix factorization, subspace clustering, and parallel factor tensor decomposition tasks motivate well algorithms for per-block-convex and non-smooth optimization problems. By leveraging the stochastic approximation paradigm and first-order acceleration schemes, this paper develops an online and modular learning algorithm for a large cla...
متن کاملBilinear Accelerated Filter Approximation
Our method approximates exact texture filtering for arbitrary scales and translations of an image while taking into account the performance characteristics of modern GPUs. Our algorithm is fast because it accesses textures with a high degree of spatial locality. Using bilinear samples guarantees that the texels we read are in a regular pattern and that we use a hardware accelerated path. We con...
متن کاملAccelerated spectral approximation
A systematic development of higher order spectral analysis, introduced by Dellwo and Friedman, is undertaken in the framework of an appropriate product space. Accelerated analogues of Osborn’s results about spectral approximation are presented. Numerical examples are given by considering an integral operator.
متن کاملAccelerated Stochastic Power Iteration
Principal component analysis (PCA) is one of the most powerful tools in machine learning. The simplest method for PCA, the power iteration, requires O(1/∆) full-data passes to recover the principal component of a matrix with eigen-gap ∆. Lanczos, a significantly more complex method, achieves an accelerated rate of O(1/ √ ∆) passes. Modern applications, however, motivate methods that only ingest...
متن کاملGPU Accelerated Stochastic Simulation
Through computational methods, biologists are able learn more about molecular biology by building accurate models. These models represent and predict the reactions among species populations within a system. One popular method to develop predictive models is to use a stochastic, Monte Carlo method developed by Gillespie called the stochastic simulation algorithm (SSA). Since this algorithm is ba...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Mathematical Statistics
سال: 1958
ISSN: 0003-4851
DOI: 10.1214/aoms/1177706705