The Resource KB4DL : building a knowledge base for deep learning, by Ruthvic Punyamurtula

KB4DL : building a knowledge base for deep learning, by Ruthvic Punyamurtula

Label
KB4DL : building a knowledge base for deep learning
Title
KB4DL
Title remainder
building a knowledge base for deep learning
Statement of responsibility
by Ruthvic Punyamurtula
Title variation
Building a knowledge base for deep learning
Creator
Contributor
Author
Degree supervisor
Subject
Genre
Language
eng
Summary
Deep Learning (DL) has received considerable attention from the AI community. However, we suffer from the lack of ability in interpretation and annotation of the outcomes from extensive and exhausting learning efforts. In this thesis, we propose an automatic metadata extraction both from DL models as well as their publications and populate ontologies in Deep Learning. The automatically populated ontology represents the state-of-the-art (SOTA) results in Deep Learning based on the metadata extracted from models (model name, accuracy, network models, output, parameters) as well as ones from publications (paper title, author, topics, images, tables, data source). The evaluation of the ontology has been conducted with the models and publications from the SOTA research publications. Studying and understanding deep learning models is the base step for any deep learning engineer and availability of many deep learning frameworks makes it difficult for a developer to understand the model implementation and to study it deeply. In addition, users have to deal with huge computational requirements of the Machine Learning world and have to invest a lot of time in analyzing the model based on every parameter. It is even more tough to find the right data to replicate or evaluate the model, as data pre-processing is a crucial step in getting the right model. To handle these situations, in this thesis, we have developed an approach to extract possible information from a research paper, link it to datasets, tag the pre- trained models and built a deep learning knowledge base out of it. We have further used Natural Language Processing techniques such as Lemmatization, POS tagging, Named Entity Recognition to process the filtered data from research articles/papers and obtain Triplet <Subject, Predicate, Object> format. These triplets are used to validate the extracted meta data and use to fill in any missing data. The application links a Model -> Source Code -> Research Paper, which has great potential in understanding the models based on their framework. We have created a web application where users can upload models/documentation, view existing models from the created knowledge base, categorize the models based on framework, language, the class name. The application also visualizes the model and its architecture in detail and also presents the user with pre-trained models (if existing). We have extracted images, tables, topics, categories, datasets and pre-trained models from various sources and linked these details to a publication. In this way, the application and its knowledge base present a system for users to host, find, compare, study and analyze the deep learning models, which help the developers in understanding the model's behavior. Using this we can also find model similarities based on architecture and this also gives scope for building a recommendation system for the future
Cataloging source
UMK
http://library.link/vocab/creatorName
Punyamurtula, Ruthvic
Degree
M.S.
Dissertation note
(School of Computing and Engineering).
Dissertation year
2019.
Granting institution
University of Missouri-Kansas City,
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorDate
1960-
http://library.link/vocab/relatedWorkOrContributorName
Lee, Yugyung
http://library.link/vocab/subjectName
  • Machine learning
  • Metadata
  • Data mining
Label
KB4DL : building a knowledge base for deep learning, by Ruthvic Punyamurtula
Instantiates
Publication
Copyright
Note
  • "A thesis in Computer Science."
  • Advisor: Yugyung Lee
  • Vita
Antecedent source
not applicable
Bibliography note
Includes bibliographical references (pages 61-65)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
black and white
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Introduction -- Background and related work -- Proposed work -- results and evaluation -- Conclusion and future work
Control code
1104844519
Dimensions
unknown
Extent
1 online resource (66 pages)
File format
one file format
Form of item
online
Level of compression
mixed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations.
Quality assurance targets
not applicable
Specific material designation
remote
System control number
(OCoLC)1104844519
System details
  • The full text of the thesis is available as an Adobe Acrobat .pdf file; Adobe Acrobat Reader required to view the file
  • Mode of access: World Wide Web
Label
KB4DL : building a knowledge base for deep learning, by Ruthvic Punyamurtula
Publication
Copyright
Note
  • "A thesis in Computer Science."
  • Advisor: Yugyung Lee
  • Vita
Antecedent source
not applicable
Bibliography note
Includes bibliographical references (pages 61-65)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
black and white
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Introduction -- Background and related work -- Proposed work -- results and evaluation -- Conclusion and future work
Control code
1104844519
Dimensions
unknown
Extent
1 online resource (66 pages)
File format
one file format
Form of item
online
Level of compression
mixed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations.
Quality assurance targets
not applicable
Specific material designation
remote
System control number
(OCoLC)1104844519
System details
  • The full text of the thesis is available as an Adobe Acrobat .pdf file; Adobe Acrobat Reader required to view the file
  • Mode of access: World Wide Web

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