Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/136271
Title: TOWARDS SCALABLE GRADIENT-BASED HYPERPARAMETER OPTIMIZATION IN DEEP NEURAL NETWORKS
Authors: FU JIE
Keywords: hyperparameter optimization, deep neural networks
Issue Date: 19-Aug-2016
Citation: FU JIE (2016-08-19). TOWARDS SCALABLE GRADIENT-BASED HYPERPARAMETER OPTIMIZATION IN DEEP NEURAL NETWORKS. ScholarBank@NUS Repository.
Abstract: It is well-known that the performance of large-sized deep neural networks (DNNs) is sensitive to the setting of their hyperparameters. Hyperparameter optimization is thus recognized as a crucial step in the process of applying DNNs to achieve best performance and drive industrial applications. The works described in this thesis represent the first forays into the scalable gradient-based methods for elementary- and hyper-parameter optimization in DNNs in a unified manner.
URI: http://scholarbank.nus.edu.sg/handle/10635/136271
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
FuJ.pdf1.52 MBAdobe PDF

OPEN

NoneView/Download

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.