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Efficiency in Machine Learning with ...

Nesky, Amy.

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Efficiency in Machine Learning with Focus on Deep Learning and Recommender Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Efficiency in Machine Learning with Focus on Deep Learning and Recommender Systems./
Author:	Nesky, Amy.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	111 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-07, Section: B.
Contained By:	Dissertations Abstracts International82-07B.
Subject:	Educational technology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28240172
ISBN:	9798684626647

Efficiency in Machine Learning with Focus on Deep Learning and Recommender Systems.
Nesky, Amy.

Efficiency in Machine Learning with Focus on Deep Learning and Recommender Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 111 p.

Source: Dissertations Abstracts International, Volume: 82-07, Section: B.

Thesis (Ph.D.)--University of Michigan, 2020.

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

Machine learning algorithms have opened up countless doors for scientists tackling problems that had previously been inaccessible, and the applications of these algorithms are far from exhausted. However, as the complexity of the learning problem grows, so does the computational and memory cost of the appropriate learning algorithm. As a result, the training process for computationally heavy algorithms can take weeks or even months to reach a good result, which can be prohibitively expensive. The general inefficiencies of machine learning algorithms is a significant bottleneck slowing the progress in application sciences. This thesis introduces three new methods of improving the efficiency of machine learning algorithms focusing on expensive algorithms such as neural networks and recommender systems. The first method discussed makes structured reductions of fully connected layers in neural networks, which causes speedup during training and decreases the amount of storage required. The second method presented is an accelerated gradient descent method called Predictor-Corrector Gradient Descent (PCGD) that combines predictor-corrector techniques with stochastic gradient descent. The final technique introduced generates Artificial Core Users (ACUs) from the Core Users of a recommendation dataset. Core Users condense the number of users in a recommendation dataset without significant loss of information; Artificial Core Users improve the recommendation accuracy of Core Users yet still mimic real user data.

ISBN: 9798684626647Subjects--Topical Terms:

517670
Educational technology.
Subjects--Index Terms:

Machine learning

Efficiency in Machine Learning with Focus on Deep Learning and Recommender Systems.
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