In a series of experiments, we show that this yields a number of interesting benefits: (1) The resulting order induced by distances in the embedding space can be used to directly explain classification decisions.
Ensuring safety of the products offered to the customers is of paramount importance to any e- commerce platform.
State-of-the-art approaches for text classification leverage a transformer architecture with a linear layer on top that outputs a class distribution for a given prediction problem.
Recent progress of deep image classification models has provided great potential to improve state-of-the-art performance in related computer vision tasks.
In this paper, we propose a semi-supervised deep learning method for detecting the specific types of reads that impede the de novo genome assembly process.