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Filtering by: Creator Boyd, Alex Remove constraint Creator: Boyd, Alex Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan

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Network Coding, Random Matrices, and Their Applications to Communication Systems

Creator:

Nguyen-Huu, Duong

Abstract:

In this work, we study network coding technique, its relation to random matrices, and their applications to communication systems. The dissertation consists of three main contributions. First, we propose efficient algorithms for data synchronization via a broadcast channel using random network coding. Second, we study the resiliency of network coding...

Resource Type:

Dissertation
Improving automated email tagging with implicit feedback

Creator:

Sorower, Mohammad Shahed

Abstract:

Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a good fit for many applications, especially in the cases where complete ground truth data is not available for offline training. In addition, batch methods do...

Resource Type:

Dissertation
Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

Creator:

Khorram, Saeed

Abstract:

In this thesis, we introduce a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by embedding a high-dimensional activation vector of a deep network layer non-linearly into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can...

Resource Type:

Masters Thesis
Parsing with Recurrent Neural Networks

Creator:

Cross, James Henry, III

Abstract:

Machine learning models for natural language processing have traditionally relied on large numbers of discrete features, built up from atomic categories such as word forms and part-of-speech labels, which are considered completely distinct from each other. Recently however, the advent of dense feature representations coupled with deep learning techniques has...

Resource Type:

Dissertation
From multitarget tracking to event recognition in videos

Creator:

Brendel, William

Abstract:

This dissertation addresses two fundamental problems in computer vision—namely, multitarget tracking and event recognition in videos. These problems are challenging because uncertainty may arise from a host of sources, including motion blur, occlusions, and dynamic cluttered backgrounds. We show that these challenges can be successfully addressed by using a multiscale,...

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Dissertation

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Academic Affiliation

Electrical Engineering and Computer Science5

Advisor

Dietterich, Thomas G.1
Huang, Liang1
Li, Fuxin1
Nguyen, Thinh1
Todorovic, Sinisa1

Commencement Year

Commencement Year range begin – Commencement Year range end

Current results range from 2011 to 2020

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Committee Member

Fern, Alan[remove]5
Fern, Xiaoli2
Raich, Raviv2
Tadepalli, Prasad2
Bose, Bella1
more Committee Members »

Creator

Brendel, William1
Cross, James Henry, Iii1
Khorram, Saeed1
Nguyen Huu, Duong1
Sorower, Mohammad Shahed1

Contributor

Fu, Xiao1
Li, Fuxin1
Qi, Zhongang1

Date

Date range begin – Date range end

Current results range from 2011 to 2020

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Decade

2020-20291
2010-20194

Degree Field

Computer Science4
Electrical and Computer Science1

Degree Level

Doctoral4
Master's1

Degree Name

Doctor of Philosophy (Ph.D.)4
Master of Science (M.S.)1

Language

English [eng]5

License

All rights reserved5

Non-Academic Affiliation

Oregon State University. Graduate School4

Peer Reviewed

No5

Resource Type

Dissertation4
Masters Thesis1

Rights Statement

In Copyright5

Subject

Machine learning2
Coding theory1
Computer vision1
Electronic mail messages1
Neural networks (Computer science)1
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