عنوان

Link Prediction with Deep Learning Models

شماره

TLpq2394984047

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Link Prediction with Deep Learning Models

نام عام مواد

[Thesis]

نام نخستين پديدآور

Aksakal, Ahmet Salih

نام ساير پديدآوران

Al Faruque, Mohammad

نام ناشر، پخش کننده و غيره

University of California, Irvine

تاریخ نشرو بخش و غیره

2019

نام خاص و کميت اثر

50

جزئيات پايان نامه و نوع درجه آن

M.S.

کسي که مدرک را اعطا کرده

University of California, Irvine

امتياز متن

2019

متن يادداشت

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.

موضوع مستند نشده

Artificial intelligence

موضوع مستند نشده

Computer engineering

موضوع مستند نشده

Computer science

مستند نام اشخاص تاييد نشده

Aksakal, Ahmet Salih

مستند نام اشخاص تاييد نشده

Al Faruque, Mohammad

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y