Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/134951
DC Field | Value | |
---|---|---|
dc.title | A TRAINING FRAMEWORK AND ARCHITECTURAL DESIGN OF DISTRIBUTED DEEP LEARNING | |
dc.contributor.author | WANG WEI | |
dc.date.accessioned | 2017-02-28T18:01:30Z | |
dc.date.available | 2017-02-28T18:01:30Z | |
dc.date.issued | 2016-08-10 | |
dc.identifier.citation | WANG WEI (2016-08-10). A TRAINING FRAMEWORK AND ARCHITECTURAL DESIGN OF DISTRIBUTED DEEP LEARNING. ScholarBank@NUS Repository. | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/134951 | |
dc.description.abstract | Deep learning has recently gained a lot of attention on account of its incredible success in many complex data-driven applications, such as image classification. However, deep learning is quite user-hostile and is thus difficult to apply. For example, it is tricky and slow to train a large model which may consume a lot of memory. This thesis introduces our investigations and approaches towards these challenges. First, we have conducted a comprehensive analysis of optimization techniques for deep learning systems, including stand-alone and distributed training. Second, we have designed and developed a distributed deep learning system, named SINGA, which tackles the usability problem and realizes optimization techniques for distributed training. SINGA provides a flexible system architecture for running different distributed training frameworks. Last, we have proposed deep learning based methods for effective multi-modal retrieval on top of SINGA, which outperform state-of-the-art approaches. | |
dc.language.iso | en | |
dc.subject | Deep Learning, Distributed Training, Multi-modal Retrieval | |
dc.type | Thesis | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.contributor.supervisor | OOI BENG CHIN | |
dc.description.degree | Ph.D | |
dc.description.degreeconferred | DOCTOR OF PHILOSOPHY | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Ph.D Theses (Open) |
Show simple item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
WangW.pdf | 7.23 MB | Adobe PDF | OPEN | None | View/Download |
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.