As Facebook struggles with fallout from the Cambridge Analytica scandal, its research arm today delivered a welcome bit of good news in deep learning. Research Engineer Dr. Yuxin Wu and Research ...

Through this article, we will discuss how the batch normalization helps in building an efficient model. Batch normalization is a feature that we add between the layers of the neural network and it ...

Batch Normalization is a pivotal technique in deep learning that significantly aids in training neural networks by mitigating issues associated with internal covariate shifts and accelerating ...

Abstract: As convolutional neural network contains many redundant parameters, a lot of methods have been developed to compress the network for accelerating inference. Among these, network pruning, ...

main.py - for the execution it is needed to choose the network for training and evaluation from the following possibilities: 'lenet' - the original LeNet network with no batch normalization involved; ...

In bulk RNA-seq analysis, normalization and batch effect removal are two necessary procedures to scale the read counts and reduce the technical errors. Many differential expression analysis tools ...

Cytometry by Time-Of-Flight (CyTOF) uses antibodies conjugated to isotopically pure metals to identify and quantify a large number of cellular features with single-cell resolution. A barcoding ...