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Filtering by: Committee Paffenroth, Randy Remove constraint Committee: Paffenroth, Randy Unit (Department) Data Science Remove constraint Unit (Department): Data Science Resource type Thesis Remove constraint Resource type: Thesis

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Anomaly Detection with Advanced Nonlinear Dimensionality Reduction

Keyword:

Anomaly Detection, Dimensionality Reduction, Manifold Learning, and Unsupervised Learning

Creator:

Beach, David J.

Advisor:

Paffenroth, Randy Clinton

Publisher:

Worcester Polytechnic Institute

Date Created:

2020-05-07

Resource Type:

Thesis

Degree:

MS

Unit (Department):

Data Science
Dimension Reduction and LASSO using Pointwise and Group Norms

Keyword:

Dimension Reduction, LASSO, and Robust Sparse PCA

Creator:

Jutras, Melanie A

Advisor:

Paffenroth, Randy Clinton

Publisher:

Worcester Polytechnic Institute

Date Created:

2018-12-12

Resource Type:

Thesis

Degree:

MS

Unit (Department):

Data Science
Parameter Continuation with Secant Approximation for Deep Neural Networks

Keyword:

non-convex, homotopy, Deep Learning, parameter continuation , Optimization, and secant

Creator:

Pathak, Harsh N.

Advisor:

Paffenroth, Randy Clinton

Publisher:

Worcester Polytechnic Institute

Date Created:

2018-12-05

Resource Type:

Thesis

Degree:

MS

Unit (Department):

Data Science
Kernel Coherence Encoders

Keyword:

Kernel methods, Autoencoders, KCCA, and CCA

Creator:

Sun, Fangzheng

Advisor:

Paffenroth, Randy Clinton

Publisher:

Worcester Polytechnic Institute

Date Created:

2018-04-23

Resource Type:

Thesis

Degree:

MS

Unit (Department):

Data Science
Compressed Sensing via Partial L1 Minimization

Keyword:

partial L1 minimization, the augmented Lagrangian method , L0 minimization, truncated L1 minimization, compressed sensing, and L1 minimization

Creator:

Zhong, Lu

Advisor:

Paffenroth, Randy Clinton

Publisher:

Worcester Polytechnic Institute

Date Created:

2017-04-27

Resource Type:

Thesis

Degree:

MS

Unit (Department):

Data Science
Robust Auto-encoders

Keyword:

Auto-encoder, Deep Learning, and Robust Auto-encoder

Creator:

Zhou, Chong

Advisor:

Paffenroth, Randy Clinton

Publisher:

Worcester Polytechnic Institute

Date Created:

2016-04-27

Resource Type:

Thesis

Degree:

MS

Unit (Department):

Data Science
Analysis and Prediction of Community Structure Using Unsupervised Learning

Keyword:

eRPCA, Community Prediction, Low Rank, and Sparse Matrix

Creator:

Biradar, Rakesh

Advisor:

Paffenroth, Randy Clinton

Publisher:

Worcester Polytechnic Institute

Date Created:

2016-01-26

Resource Type:

Thesis

Degree:

MS

Unit (Department):

Data Science
Large Scale Matrix Completion and Recommender Systems

Keyword:

low rank matrix, Principal Component Analysis, robust principal component analysis, matrix completion, recommender systems, and convex relaxation

Creator:

Amadeo, Lily

Advisor:

Paffenroth, Randy Clinton

Publisher:

Worcester Polytechnic Institute

Date Created:

2015-09-04

Resource Type:

Thesis

Degree:

MS

Unit (Department):

Data Science
Multi-modal Deep Learning

Keyword:

multi-modal and deep learning

Creator:

Liu, Si

Advisor:

Paffenroth, Randy Clinton

Publisher:

Worcester Polytechnic Institute

Date Created:

2022-04-26

Resource Type:

Thesis

Degree:

MS

Unit (Department):

Data Science
Cycles Improve Conditional Generators

Keyword:

GAN, Conditional, Deep Learning, Generation, Cycle, and Machine Leaning

Creator:

Moore, Alexander M.

Advisor:

Paffenroth, Randy Clinton and Kong, Xiangnan

Publisher:

Worcester Polytechnic Institute

Date Created:

2021-04-30

Resource Type:

Thesis

Degree:

MS

Unit (Department):

Data Science

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Masters Theses18

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Amadeo, Lily1
Barger, Maria1
Beach, David J.1
Biradar, Rakesh1
Buquicchio, Luke J.1
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Advisor

Agu, Emmanuel O.2
Harrison, Lane T.1
Kong, Xiangnan2
Mangoubi, Oren Rami1
Paffenroth, Randy Clinton13
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Agu, Emmanuel O.2
Harrison, Lane T.1
Kong, Xiangnan3
Liu, Xiaozhong1
Mangoubi, Oren Rami1
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Unit (Department)

Data Science[remove]18

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Worcester Polytechnic Institute18

Resource type

Thesis[remove]18

License

https://creativecommons.org/licenses/by-nc/4.0/1

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