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The Bayesian ARTMAP neural network, introduced by Vigdor and Lerner, is an incremental learning algorithm which can efficiently process massive datasets for classification, regression, and probabilistic inference tasks. We introduce the parallelized version of the BA neural network and implement it in OpenCL. Our implementation runs on both multi-core CPUs and GPUs architectures. We test the Parallel...
Several neural architectures were successfully used to predict properties of chemical compounds. Obtaining satisfactory results with neural networks depends on the availability of large data samples. However, most classical Quantitative Structure-Activity Relationship studies have been performed on small datasets. Neural models do generally infer with difficulty from such datasets. In our study, we...
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