Sentence examples for existing inference from inspiring English sources
We present an implementation in OpenCL, which is able to exploit the multicore architecture of modern GPUs to compute a large number of samples in parallel, and demonstrate that this is competitive with existing inference algorithms.
The goals of this paper are to explore the field of such applications, to figure out the advantages of inference ontologies in a PLM context and to synthesize major existing inference models in terms of methodology and structuration.
(Indeed, this is one of the benefits of implementing a method using an existing inference platform).
Many existing inference methods rely on multidimensional scaling (MDS), in which the pairwise distances of the inferred model are optimized to resemble pairwise distances derived directly from the contact counts.
High performance of our proposed algorithm as compared to existing BN inference algorithms that depend on inference of connectivity from the data, further support the advantage of using prior biological knowledge on connectivity.
Machine learning, data mining, and pattern recognition algorithms are based on existing statistical inference models.
The main practical impetus behind such approaches is to be able to exploit existing FOL inference techniques and technology.
Existing parametric inference methods for estimating spread in 3-D rotations are compared to the bootstrapping procedure through a simulation study.
To overcome this problem, this paper proposes a two-step framework, Clustering Embedded Network Inference (CENI), which incorporates the cluster structures of nodes to improve the efficiency of existing network inference algorithm.
Therefore, we show that the proposed framework may be applied in combination with other existing GRN inference methods to improve the prediction accuracy of networks with scale-free properties.
In this paper, we describe some of the existing multivariate inference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, large-scale data.
