عنوان

Link Prediction with Deep Learning Models

Number

TLpq2394984047

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Link Prediction with Deep Learning Models

General Material Designation

[Thesis]

First Statement of Responsibility

Aksakal, Ahmet Salih

Subsequent Statement of Responsibility

Al Faruque, Mohammad

Name of Publisher, Distributor, etc.

University of California, Irvine

Date of Publication, Distribution, etc.

2019

Specific Material Designation and Extent of Item

50

Dissertation or thesis details and type of degree

M.S.

Body granting the degree

University of California, Irvine

Text preceding or following the note

2019

Text of Note

Deep Learning has been used extensively in many applications by researchers. With the increased attraction to Deep Learning, more and more unique models are created each year. However, sometimes some of the model details are not included in the publications. This makes using new Deep Learning models for research a time-consuming task for researchers. In order to tackle with this problem, we propose a prediction mechanism for the missing information in the model. By creating a dataset where the Deep Learning models are represented as knowledge graphs, we made it possible to use knowledge graph embedding algorithms which are specifically designed for eliminating missing information in a given data. We inspected 6 different algorithms and compared their performances on a small-scale experiment. After the comparison, we picked the most promising algorithm and used it for link prediction in Deep Learning models.

Artificial intelligence

Computer engineering

Computer science

Aksakal, Ahmet Salih

Al Faruque, Mohammad

Electronic name

p

[Thesis]

276903

a

Y