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Cover image for Latent Variable Models for Understanding User Behavior in Software Applications

Latent Variable Models for Understanding User Behavior in Software Applications

13.

Latent Variable Models for Understanding User Behavior in Software Applications

by

Saeedi, Ardavan, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10902470

Format:

Excerpt:

models; I introduce models and inference algorithms for time series segmentation which are scalable to

Cover image for Using Electronic Health Records to Identify Incarceration History among Veterans

Using Electronic Health Records to Identify Incarceration History among Veterans

14.

Using Electronic Health Records to Identify Incarceration History among Veterans

by

Kim, Clara Heown, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10747399

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Excerpt:

a keyword search. Both methods are much more sensitive than a human reader. ML algorithms may thus

Cover image for A Study of Ensemble Learning with AdaBoost and NEAT

A Study of Ensemble Learning with AdaBoost and NEAT

15.

A Study of Ensemble Learning with AdaBoost and NEAT

by

Schukei, Robert, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10606227

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Excerpt:

Neural networks, ensemble algorithms, neuroevolution, and genetic algorithms all have shown the

Cover image for Adaptation and Augmentation: Towards Better Rescoring Strategies for Automatic Speech Recognition and Spoken Term Detection

Adaptation and Augmentation: Towards Better Rescoring Strategies for Automatic Speech Recognition and Spoken Term Detection

16.

Adaptation and Augmentation: Towards Better Rescoring Strategies for Automatic Speech Recognition and Spoken Term Detection

by

Ma, Min, author. (orcid)0000-0002-1816-1772

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10792971

Format:

Excerpt:

significant improvements by using the source application information. (3) Marrying machine learning algorithms

Cover image for Machine Learning-assisted Prediction of Structure and Function of Cystine-stabilized Peptides and Optimization of Expression in an E. coli System

Machine Learning-assisted Prediction of Structure and Function of Cystine-stabilized Peptides and Optimization of Expression in an E. coli System

17.

Machine Learning-assisted Prediction of Structure and Function of Cystine-stabilized Peptides and Optimization of Expression in an E. coli System

by

Islam, S. M. Ashiqul, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10794048

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Excerpt:

-assisted algorithms PredSTP and CSPred to predict structural and functional characteristics, respectively, of cystine

Cover image for Machine Learning-Assisted Prediction of Structure and Function of Cystine-Stabilized peptides and Optimization of Expression in an E. coli System

Machine Learning-Assisted Prediction of Structure and Function of Cystine-Stabilized peptides and Optimization of Expression in an E. coli System

18.

Machine Learning-Assisted Prediction of Structure and Function of Cystine-Stabilized peptides and Optimization of Expression in an E. coli System

by

Islam, S. M. Ashiqul, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10784140

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Excerpt:

To contribute to developing this pipeline, I developed the machine learning-assisted algorithms

Cover image for Investigation of Deep Neural Network Image Processing for Cubesat Size Satellites

Investigation of Deep Neural Network Image Processing for Cubesat Size Satellites

19.

Investigation of Deep Neural Network Image Processing for Cubesat Size Satellites

by

Braun, Adam D., author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10790236

Format:

Excerpt:

CubeSats to utilize AI algorithms. These computationally intensive AI algorithms, such as Deep Learning

Cover image for A Study of Interpretability Mechanisms for Deep Networks

A Study of Interpretability Mechanisms for Deep Networks

20.

A Study of Interpretability Mechanisms for Deep Networks

by

Kokate, Apurva, author. (orcid)0000-0003-2353-4171

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10793172

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Excerpt:

characteristics for any interpretability mechanism. We demonstrate our framework using two well-known algorithms

Cover image for Augmented Translation Models for Sequential Recommendation

Augmented Translation Models for Sequential Recommendation

21.

Augmented Translation Models for Sequential Recommendation

by

Pasricha, Rajiv, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10823252

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Excerpt:

recommendation tasks by adapting ideas from metric learning and knowledge-graph completion. These algorithms

Cover image for Domain-Specific Knowledge Exploration with Ontology Hierarchical Re-ranking and Adaptive Learning and Extension

Domain-Specific Knowledge Exploration with Ontology Hierarchical Re-ranking and Adaptive Learning and Extension

22.

Domain-Specific Knowledge Exploration with Ontology Hierarchical Re-ranking and Adaptive Learning and Extension

by

Zhao, Grace, author. (orcid)0000-0002-3998-0988

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10825522

Format:

Excerpt:

highly relevant web resources via a set of domain-centric re-ranking algorithms and adaptive ontology

Cover image for Power/Performance Modeling and Optimization: Using and Characterizing Machine Learning Applications

Power/Performance Modeling and Optimization: Using and Characterizing Machine Learning Applications

23.

Power/Performance Modeling and Optimization: Using and Characterizing Machine Learning Applications

by

Cai, Ermao, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10827562

Format:

Excerpt:

algorithms or optimization techniques. To have accurate models, one needs to consider not only technology

Cover image for Novelty Detection of Machinery Using a Non-parametric Machine Learning Approach

Novelty Detection of Machinery Using a Non-parametric Machine Learning Approach

24.

Novelty Detection of Machinery Using a Non-parametric Machine Learning Approach

by

Angola Abreu, Enrique D., author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10829768

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Excerpt:

bins. This process eliminates the need for data dimension reduction algorithms. The method of "pseudo

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