Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Entire DC Network
Model-Based Deep Autoencoders For Clustering Single-Cell Rna Sequencing Data With Side Information, Xiang Lin
Model-Based Deep Autoencoders For Clustering Single-Cell Rna Sequencing Data With Side Information, Xiang Lin
Dissertations
Clustering analysis has been conducted extensively in single-cell RNA sequencing (scRNA-seq) studies. scRNA-seq can profile tens of thousands of genes' activities within a single cell. Thousands or tens of thousands of cells can be captured simultaneously in a typical scRNA-seq experiment. Biologists would like to cluster these cells for exploring and elucidating cell types or subtypes. Numerous methods have been designed for clustering scRNA-seq data. Yet, single-cell technologies develop so fast in the past few years that those existing methods do not catch up with these rapid changes and fail to fully fulfil their potential. For instance, besides profiling transcription …
Loss Scaling And Step Size In Deep Learning Optimizatio, Nora Alosily
Loss Scaling And Step Size In Deep Learning Optimizatio, Nora Alosily
Dissertations
Deep learning training consumes ever-increasing time and resources, and that is
due to the complexity of the model, the number of updates taken to reach good
results, and both the amount and dimensionality of the data. In this dissertation,
we will focus on making the process of training more efficient by focusing on the
step size to reduce the number of computations for parameters in each update.
We achieved our objective in two new ways: we use loss scaling as a proxy for
the learning rate, and we use learnable layer-wise optimizers. Although our work
is perhaps not the first …