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The hybrid nature of the algorithm seeks to maximize computation between communication and synchronization.
Iterative reconstruction is a backprojection/forward projection process, where the algorithm seeks to improve the current image estimate with each iteration [3].
Rather than finding motifs over-represented in a set of proteins relative to a background set, the algorithm seeks to discover motifs informative of measurements or behaviors associated with each protein.
The Iterative Closest Point (ICP) algorithm seeks to minimize the misalignment between two point cloud data sets.
The manifold learning algorithm seeks to keep local relationship of the feature matrix, which is more meaningful for damage assessment.
Our algorithm seeks to build resiliency by maintaining a necessary set of working nodes and replacing failed ones when needed.
The algorithm seeks to minimize the error between reduced and detailed chemistry for simple reactor calculations approximating conditions of interest for a computational fluid dynamics simulation.
We consider a class of problems in which an algorithm seeks to compute a function f over a set of n inputs, where each input has an associated price.
While other social discovery apps link people based on shared interests, friends or location, Civo's algorithm seeks to maximize the geographical distance between partners.
The EM algorithm seeks to classify the (y_n) data on classes, or Gaussian, and, later, to re-estimate each class value.
The BigClam algorithm seeks to fit a probabilistic generative model that satisfies certain community properties discovered in their studies [1, 24].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com