Sentence examples for machine learning architecture from inspiring English sources

That's why companies should invest early in machine learning architecture that continually collects new data and uses it to regularly update its AI models.

This bio-inspired machine learning architecture for dictionary learning and sparse classification permits fewer costly physical strain sensors while being simultaneously robust to sensor noise.

From there, we go on to the Hadoop ecosystem for a look at many of the projects that are part of a typical machine learning architecture and an understanding of how everything might fit together.

We pass the entire multi-band galaxy image into the machine learning architecture to obtain a redshift estimate that is competitive, in terms of the measured point prediction metrics, with the best existing standard machine learning techniques.

We use different machine learning architectures in order to investigate the accuracy and performance within the case study.

Existing human action classification systems use machine learning architectures and low-level features to characterize a specific human action.

However, existing machine learning architectures often lack restructuring capability, which is an important process of forming the conceptual structures in human-level synthetic intelligence.

However we do note that Deep Neural Networks require many orders of magnitude more computing resources than standard machine learning architectures, and as such are only tractable for making predictions on datasets of size ≤50k before implementing parallelisation techniques.

This work shows that more attention should be given to the influence of machine learning model architectures on the accuracy of PEMS models, as the computational cost of the GA is well justified by the resulting high accuracy.

Unlike traditional studies primarily relying on cautious nodule segmentation and time-consuming feature extraction, we tackle a more challenging task on directly modeling raw nodule patches and building an end-to-end machine-learning architecture for classifying lung nodule malignancy suspiciousness.

This paper explores different machine learning algorithms and architectures for classifying lithologies (e.g. coal and sandstone) using wireline data from a project area well-known for coal mineralisation: Juandah East, 60 km north-west of Wandoan (Queensland, Australia).