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Preventing Overfitting in Deep Learn...

Khatri, Alizishaan Anwar Hussein.

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Preventing Overfitting in Deep Learning Using Differential Privacy.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Preventing Overfitting in Deep Learning Using Differential Privacy./
Author:	Khatri, Alizishaan Anwar Hussein.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	51 p.
Notes:	Source: Masters Abstracts International, Volume: 79-07.
Contained By:	Masters Abstracts International79-07.
Subject:	Computer Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10622959
ISBN:	9780355311082

Preventing Overfitting in Deep Learning Using Differential Privacy.
Khatri, Alizishaan Anwar Hussein.

Preventing Overfitting in Deep Learning Using Differential Privacy. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 51 p.

Source: Masters Abstracts International, Volume: 79-07.

Thesis (M.S.)--State University of New York at Buffalo, 2017.

This item must not be sold to any third party vendors.

The use of Deep Neural Network based systems in the real world is growing. They have achieved state-of-the-art performance on many image, speech and text datasets. They have been shown to be powerful systems that are capable of learning detailed relationships and abstractions from the data. This is a double-edged sword which makes such systems vulnerable to learning the noise in the training set, thereby negatively impacting performance. This is also known as the problem of overfitting or poor generalization. In a practical setting, analysts typically have limited data to build models that must generalize to unseen data. In this work, we explore the use of a differential-privacy based approach to improve generalization in Deep Neural Networks.

ISBN: 9780355311082Subjects--Topical Terms:

1567821
Computer Engineering.

Preventing Overfitting in Deep Learning Using Differential Privacy.
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500 $a Source: Masters Abstracts International, Volume: 79-07.
500 $a Publisher info.: Dissertation/Thesis.
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502 $a Thesis (M.S.)--State University of New York at Buffalo, 2017.
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520 $a The use of Deep Neural Network based systems in the real world is growing. They have achieved state-of-the-art performance on many image, speech and text datasets. They have been shown to be powerful systems that are capable of learning detailed relationships and abstractions from the data. This is a double-edged sword which makes such systems vulnerable to learning the noise in the training set, thereby negatively impacting performance. This is also known as the problem of overfitting or poor generalization. In a practical setting, analysts typically have limited data to build models that must generalize to unseen data. In this work, we explore the use of a differential-privacy based approach to improve generalization in Deep Neural Networks.
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