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You searched for subject:(Convolutional neural networks). Showing records 1 – 30 of 327 total matches.

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University of Illinois – Urbana-Champaign

1. Shi, Honghui. Galaxy classification with deep convolutional neural networks.

Degree: MS, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 Galaxy classification, using digital images captured from sky surveys to determine the galaxy morphological classes, is of great interest to astronomy researchers. Conventional methods rely… (more)

Subjects/Keywords: Galaxy Classification; Convolutional Neural Networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shi, H. (2016). Galaxy classification with deep convolutional neural networks. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90939

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Shi, Honghui. “Galaxy classification with deep convolutional neural networks.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed January 22, 2020. http://hdl.handle.net/2142/90939.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Shi, Honghui. “Galaxy classification with deep convolutional neural networks.” 2016. Web. 22 Jan 2020.

Vancouver:

Shi H. Galaxy classification with deep convolutional neural networks. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/2142/90939.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shi H. Galaxy classification with deep convolutional neural networks. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90939

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

2. Bowley, Connor Ryan. Training Convolutional Neural Networks Using An Automated Feedback Loop To Estimate The Population Of Avian Species.

Degree: MS, Computer Science, 2017, University of North Dakota

  Using automated processes to detect wildlife in uncontrolled outdoor imagery in the field of wildlife ecology is challenging task. This is especially true in… (more)

Subjects/Keywords: Convolutional Neural Networks; Image Processing

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APA (6th Edition):

Bowley, C. R. (2017). Training Convolutional Neural Networks Using An Automated Feedback Loop To Estimate The Population Of Avian Species. (Masters Thesis). University of North Dakota. Retrieved from https://commons.und.edu/theses/2173

Chicago Manual of Style (16th Edition):

Bowley, Connor Ryan. “Training Convolutional Neural Networks Using An Automated Feedback Loop To Estimate The Population Of Avian Species.” 2017. Masters Thesis, University of North Dakota. Accessed January 22, 2020. https://commons.und.edu/theses/2173.

MLA Handbook (7th Edition):

Bowley, Connor Ryan. “Training Convolutional Neural Networks Using An Automated Feedback Loop To Estimate The Population Of Avian Species.” 2017. Web. 22 Jan 2020.

Vancouver:

Bowley CR. Training Convolutional Neural Networks Using An Automated Feedback Loop To Estimate The Population Of Avian Species. [Internet] [Masters thesis]. University of North Dakota; 2017. [cited 2020 Jan 22]. Available from: https://commons.und.edu/theses/2173.

Council of Science Editors:

Bowley CR. Training Convolutional Neural Networks Using An Automated Feedback Loop To Estimate The Population Of Avian Species. [Masters Thesis]. University of North Dakota; 2017. Available from: https://commons.und.edu/theses/2173


University of Sydney

3. Lin, Shan. Analysing Generalisation Error Bounds For Convolutional Neural Networks .

Degree: 2018, University of Sydney

 Analysing Generalisation Error Bounds for Convolutional Neural Networks Abstract: Convolutional neural networks (CNNs) have achieved breakthrough performance in a wide range of applications including image… (more)

Subjects/Keywords: generalisation bound; convolutional neural networks

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APA (6th Edition):

Lin, S. (2018). Analysing Generalisation Error Bounds For Convolutional Neural Networks . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/20315

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lin, Shan. “Analysing Generalisation Error Bounds For Convolutional Neural Networks .” 2018. Thesis, University of Sydney. Accessed January 22, 2020. http://hdl.handle.net/2123/20315.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lin, Shan. “Analysing Generalisation Error Bounds For Convolutional Neural Networks .” 2018. Web. 22 Jan 2020.

Vancouver:

Lin S. Analysing Generalisation Error Bounds For Convolutional Neural Networks . [Internet] [Thesis]. University of Sydney; 2018. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/2123/20315.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lin S. Analysing Generalisation Error Bounds For Convolutional Neural Networks . [Thesis]. University of Sydney; 2018. Available from: http://hdl.handle.net/2123/20315

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


UCLA

4. Liu, Yingzhu. Face Aging Using Deep Convolutional Generative Adversarial Network with Condition.

Degree: Statistics, 2019, UCLA

 We explore multiple ideas on face aging, and we finally settle down on constructing a Face Reconstruction Convolutional Neural Network and a Feature Vector Encoder.… (more)

Subjects/Keywords: Statistics; Convolutional Neural Networks; Generative Adversarial Networks

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APA (6th Edition):

Liu, Y. (2019). Face Aging Using Deep Convolutional Generative Adversarial Network with Condition. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/88j0p2nx

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Liu, Yingzhu. “Face Aging Using Deep Convolutional Generative Adversarial Network with Condition.” 2019. Thesis, UCLA. Accessed January 22, 2020. http://www.escholarship.org/uc/item/88j0p2nx.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Liu, Yingzhu. “Face Aging Using Deep Convolutional Generative Adversarial Network with Condition.” 2019. Web. 22 Jan 2020.

Vancouver:

Liu Y. Face Aging Using Deep Convolutional Generative Adversarial Network with Condition. [Internet] [Thesis]. UCLA; 2019. [cited 2020 Jan 22]. Available from: http://www.escholarship.org/uc/item/88j0p2nx.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu Y. Face Aging Using Deep Convolutional Generative Adversarial Network with Condition. [Thesis]. UCLA; 2019. Available from: http://www.escholarship.org/uc/item/88j0p2nx

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Guelph

5. Olpin, Alexander J. Convolutional Networks for Segmentation and Detection of Agricultural Mushrooms .

Degree: 2018, University of Guelph

 Previous research into agricultural crop identification has used standard image processing techniques to locate crops within image data. In this work we conducted two sets… (more)

Subjects/Keywords: Machine Vision; Agriculture; Convolutional Neural Networks; Fully Convolutional Networks; Region-based Convolutional Networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Olpin, A. J. (2018). Convolutional Networks for Segmentation and Detection of Agricultural Mushrooms . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/13534

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Olpin, Alexander J. “Convolutional Networks for Segmentation and Detection of Agricultural Mushrooms .” 2018. Thesis, University of Guelph. Accessed January 22, 2020. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/13534.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Olpin, Alexander J. “Convolutional Networks for Segmentation and Detection of Agricultural Mushrooms .” 2018. Web. 22 Jan 2020.

Vancouver:

Olpin AJ. Convolutional Networks for Segmentation and Detection of Agricultural Mushrooms . [Internet] [Thesis]. University of Guelph; 2018. [cited 2020 Jan 22]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/13534.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Olpin AJ. Convolutional Networks for Segmentation and Detection of Agricultural Mushrooms . [Thesis]. University of Guelph; 2018. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/13534

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Alberta

6. Hess, Andy T. Deep Synthetic Viewpoint Prediction.

Degree: MS, Department of Computing Science, 2015, University of Alberta

 Determining the viewpoint (pose) of rigid objects in images is a classic vision problem with applications to robotic grasping, autonomous navigation, augmented reality, semantic SLAM… (more)

Subjects/Keywords: Convolutional Neural Networks; Computer Vision; Viewpoint Prediction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hess, A. T. (2015). Deep Synthetic Viewpoint Prediction. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/9g54xm59d

Chicago Manual of Style (16th Edition):

Hess, Andy T. “Deep Synthetic Viewpoint Prediction.” 2015. Masters Thesis, University of Alberta. Accessed January 22, 2020. https://era.library.ualberta.ca/files/9g54xm59d.

MLA Handbook (7th Edition):

Hess, Andy T. “Deep Synthetic Viewpoint Prediction.” 2015. Web. 22 Jan 2020.

Vancouver:

Hess AT. Deep Synthetic Viewpoint Prediction. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2020 Jan 22]. Available from: https://era.library.ualberta.ca/files/9g54xm59d.

Council of Science Editors:

Hess AT. Deep Synthetic Viewpoint Prediction. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/9g54xm59d


Texas State University – San Marcos

7. Jaradat, Farah Bilal. A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks.

Degree: MS, Engineering, 2019, Texas State University – San Marcos

 In this work, an approach is proposed to detect people and pets trapped in burning sites. This will be done in a manner that will… (more)

Subjects/Keywords: Convolutional neural networks; Firefighting assistance; Image recognition

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jaradat, F. B. (2019). A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/9010

Chicago Manual of Style (16th Edition):

Jaradat, Farah Bilal. “A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks.” 2019. Masters Thesis, Texas State University – San Marcos. Accessed January 22, 2020. https://digital.library.txstate.edu/handle/10877/9010.

MLA Handbook (7th Edition):

Jaradat, Farah Bilal. “A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks.” 2019. Web. 22 Jan 2020.

Vancouver:

Jaradat FB. A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks. [Internet] [Masters thesis]. Texas State University – San Marcos; 2019. [cited 2020 Jan 22]. Available from: https://digital.library.txstate.edu/handle/10877/9010.

Council of Science Editors:

Jaradat FB. A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks. [Masters Thesis]. Texas State University – San Marcos; 2019. Available from: https://digital.library.txstate.edu/handle/10877/9010


Vanderbilt University

8. Paul, Justin Stuart. Deep Learning for Brain Tumor Classification.

Degree: MS, Computer Science, 2016, Vanderbilt University

 Deep learning has been used successfully in supervised classification tasks in order to learn complex patterns. The purpose of the study is to apply this… (more)

Subjects/Keywords: deep learning; convolutional neural networks; overfitting

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APA (6th Edition):

Paul, J. S. (2016). Deep Learning for Brain Tumor Classification. (Masters Thesis). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-04112016-224926/ ;

Chicago Manual of Style (16th Edition):

Paul, Justin Stuart. “Deep Learning for Brain Tumor Classification.” 2016. Masters Thesis, Vanderbilt University. Accessed January 22, 2020. http://etd.library.vanderbilt.edu/available/etd-04112016-224926/ ;.

MLA Handbook (7th Edition):

Paul, Justin Stuart. “Deep Learning for Brain Tumor Classification.” 2016. Web. 22 Jan 2020.

Vancouver:

Paul JS. Deep Learning for Brain Tumor Classification. [Internet] [Masters thesis]. Vanderbilt University; 2016. [cited 2020 Jan 22]. Available from: http://etd.library.vanderbilt.edu/available/etd-04112016-224926/ ;.

Council of Science Editors:

Paul JS. Deep Learning for Brain Tumor Classification. [Masters Thesis]. Vanderbilt University; 2016. Available from: http://etd.library.vanderbilt.edu/available/etd-04112016-224926/ ;


ETH Zürich

9. Neil, Daniel. Deep Neural Networks and Hardware Systems for Event-driven Data.

Degree: 2017, ETH Zürich

 Event-based sensors, built with biological inspiration, differ greatly from traditional sensor types. A standard vision sensor uses a pixel array to produce a frame containing… (more)

Subjects/Keywords: Deep Neural Networks; Event-driven sensors; Deep neural networks (DNNs); Spiking deep neural networks; Recurrent Neural Networks; Convolutional neural networks

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APA (6th Edition):

Neil, D. (2017). Deep Neural Networks and Hardware Systems for Event-driven Data. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/168865

Chicago Manual of Style (16th Edition):

Neil, Daniel. “Deep Neural Networks and Hardware Systems for Event-driven Data.” 2017. Doctoral Dissertation, ETH Zürich. Accessed January 22, 2020. http://hdl.handle.net/20.500.11850/168865.

MLA Handbook (7th Edition):

Neil, Daniel. “Deep Neural Networks and Hardware Systems for Event-driven Data.” 2017. Web. 22 Jan 2020.

Vancouver:

Neil D. Deep Neural Networks and Hardware Systems for Event-driven Data. [Internet] [Doctoral dissertation]. ETH Zürich; 2017. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/20.500.11850/168865.

Council of Science Editors:

Neil D. Deep Neural Networks and Hardware Systems for Event-driven Data. [Doctoral Dissertation]. ETH Zürich; 2017. Available from: http://hdl.handle.net/20.500.11850/168865


University of Colorado

10. Dronen, Nicholas A. Correcting Writing Errors with Convolutional Neural Networks.

Degree: PhD, Computer Science, 2016, University of Colorado

Convolutional neural networks (ConvNets) have been shown to be effective at a variety of natural language processing tasks. To date, their utility for correcting errors… (more)

Subjects/Keywords: Convolutional neural networks; Grammar; Language models; Neural networks; Spelling; Computer Sciences

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APA (6th Edition):

Dronen, N. A. (2016). Correcting Writing Errors with Convolutional Neural Networks. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/csci_gradetds/126

Chicago Manual of Style (16th Edition):

Dronen, Nicholas A. “Correcting Writing Errors with Convolutional Neural Networks.” 2016. Doctoral Dissertation, University of Colorado. Accessed January 22, 2020. https://scholar.colorado.edu/csci_gradetds/126.

MLA Handbook (7th Edition):

Dronen, Nicholas A. “Correcting Writing Errors with Convolutional Neural Networks.” 2016. Web. 22 Jan 2020.

Vancouver:

Dronen NA. Correcting Writing Errors with Convolutional Neural Networks. [Internet] [Doctoral dissertation]. University of Colorado; 2016. [cited 2020 Jan 22]. Available from: https://scholar.colorado.edu/csci_gradetds/126.

Council of Science Editors:

Dronen NA. Correcting Writing Errors with Convolutional Neural Networks. [Doctoral Dissertation]. University of Colorado; 2016. Available from: https://scholar.colorado.edu/csci_gradetds/126


NSYSU

11. Wang, Hao-Yi. The Impacts of Image Contexts on Dialogue Systems.

Degree: Master, Information Management, 2018, NSYSU

 Chatting with machines is not only possible but also more and more common in our lives these days. With the approach, we can execute commands… (more)

Subjects/Keywords: , Dialogue; Convolutional neural networks; Recurrent neural networks; Image recognition; Natural language; Neural networks; Machine learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, H. (2018). The Impacts of Image Contexts on Dialogue Systems. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0616118-181354

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Hao-Yi. “The Impacts of Image Contexts on Dialogue Systems.” 2018. Thesis, NSYSU. Accessed January 22, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0616118-181354.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Hao-Yi. “The Impacts of Image Contexts on Dialogue Systems.” 2018. Web. 22 Jan 2020.

Vancouver:

Wang H. The Impacts of Image Contexts on Dialogue Systems. [Internet] [Thesis]. NSYSU; 2018. [cited 2020 Jan 22]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0616118-181354.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang H. The Impacts of Image Contexts on Dialogue Systems. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0616118-181354

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Ontario Institute of Technology

12. Joseph, Tony. Joint spatial and layer attention for convolutional networks.

Degree: 2019, University of Ontario Institute of Technology

 In this work, we propose a novel approach that learns to sequentially attend to different Convolutional Neural Networks (CNN) layers (i.e., ???what??? feature abstraction to… (more)

Subjects/Keywords: Computational Attention; Convolutional Neural Networks; Reccurent Neural Networks; Neural Networks; Image-Based Camera Localization

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APA (6th Edition):

Joseph, T. (2019). Joint spatial and layer attention for convolutional networks. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/1061

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Joseph, Tony. “Joint spatial and layer attention for convolutional networks.” 2019. Thesis, University of Ontario Institute of Technology. Accessed January 22, 2020. http://hdl.handle.net/10155/1061.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Joseph, Tony. “Joint spatial and layer attention for convolutional networks.” 2019. Web. 22 Jan 2020.

Vancouver:

Joseph T. Joint spatial and layer attention for convolutional networks. [Internet] [Thesis]. University of Ontario Institute of Technology; 2019. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/10155/1061.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Joseph T. Joint spatial and layer attention for convolutional networks. [Thesis]. University of Ontario Institute of Technology; 2019. Available from: http://hdl.handle.net/10155/1061

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universitat Pompeu Fabra

13. Pons Puig, Jordi. Deep neural networks for music and audio tagging.

Degree: Departament de Tecnologies de la Informació i les Comunicacions, 2019, Universitat Pompeu Fabra

 L’etiquetatge automàtic d’àudio i de música pot augmentar les possibilitats de reutilització de moltes de les bases de dades d’àudio que romanen pràcticament sense etiquetar.… (more)

Subjects/Keywords: Deep learning; Music; Audio; Deep neural networks; Transfer learning; Prototypical networks; Convolutional neural networks; Randomly weighted neural networks; Musically motivated convolutional neural networks; End-to-end learning; 62

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APA (6th Edition):

Pons Puig, J. (2019). Deep neural networks for music and audio tagging. (Thesis). Universitat Pompeu Fabra. Retrieved from http://hdl.handle.net/10803/668036

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Pons Puig, Jordi. “Deep neural networks for music and audio tagging.” 2019. Thesis, Universitat Pompeu Fabra. Accessed January 22, 2020. http://hdl.handle.net/10803/668036.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Pons Puig, Jordi. “Deep neural networks for music and audio tagging.” 2019. Web. 22 Jan 2020.

Vancouver:

Pons Puig J. Deep neural networks for music and audio tagging. [Internet] [Thesis]. Universitat Pompeu Fabra; 2019. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/10803/668036.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pons Puig J. Deep neural networks for music and audio tagging. [Thesis]. Universitat Pompeu Fabra; 2019. Available from: http://hdl.handle.net/10803/668036

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


San Jose State University

14. Deshmukh, Kunal Rajan. Image Compression Using Neural Networks.

Degree: MS, Computer Science, 2019, San Jose State University

  Image compression is a well-studied field of Computer Vision. Recently, many neural network based architectures have been proposed for image compression as well as… (more)

Subjects/Keywords: Convolutional Neural Networks; Generative Adversarial Networks; Artificial Intelligence and Robotics

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APA (6th Edition):

Deshmukh, K. R. (2019). Image Compression Using Neural Networks. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.h8mt-65ct ; https://scholarworks.sjsu.edu/etd_projects/666

Chicago Manual of Style (16th Edition):

Deshmukh, Kunal Rajan. “Image Compression Using Neural Networks.” 2019. Masters Thesis, San Jose State University. Accessed January 22, 2020. https://doi.org/10.31979/etd.h8mt-65ct ; https://scholarworks.sjsu.edu/etd_projects/666.

MLA Handbook (7th Edition):

Deshmukh, Kunal Rajan. “Image Compression Using Neural Networks.” 2019. Web. 22 Jan 2020.

Vancouver:

Deshmukh KR. Image Compression Using Neural Networks. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2020 Jan 22]. Available from: https://doi.org/10.31979/etd.h8mt-65ct ; https://scholarworks.sjsu.edu/etd_projects/666.

Council of Science Editors:

Deshmukh KR. Image Compression Using Neural Networks. [Masters Thesis]. San Jose State University; 2019. Available from: https://doi.org/10.31979/etd.h8mt-65ct ; https://scholarworks.sjsu.edu/etd_projects/666


Edith Cowan University

15. Caldera, Shehan. Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot.

Degree: 2019, Edith Cowan University

 Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. The… (more)

Subjects/Keywords: CNN; convolutional neural networks; deep learning; DCNN; deep convolutional neural networks; robotic grasping; grasp detection; Artificial Intelligence and Robotics

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APA (6th Edition):

Caldera, S. (2019). Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot. (Thesis). Edith Cowan University. Retrieved from https://ro.ecu.edu.au/theses/2170

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Caldera, Shehan. “Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot.” 2019. Thesis, Edith Cowan University. Accessed January 22, 2020. https://ro.ecu.edu.au/theses/2170.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Caldera, Shehan. “Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot.” 2019. Web. 22 Jan 2020.

Vancouver:

Caldera S. Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot. [Internet] [Thesis]. Edith Cowan University; 2019. [cited 2020 Jan 22]. Available from: https://ro.ecu.edu.au/theses/2170.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Caldera S. Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot. [Thesis]. Edith Cowan University; 2019. Available from: https://ro.ecu.edu.au/theses/2170

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of California – San Diego

16. Tripathi, Subarna. Improving Object Detection and Segmentation by Utilizing Context.

Degree: Electrical Engineering (Signal and Image Proc), 2018, University of California – San Diego

 Object detection and segmentation are important computer vision problems that have applications in several domains such as autonomous driving, virtual and augmented reality systems, human-computer… (more)

Subjects/Keywords: Computer science; Convolutional Neural Networks; Deep Learning; Object Detection; Recurrent Neural Networks; Segmentation; Video Processing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Tripathi, S. (2018). Improving Object Detection and Segmentation by Utilizing Context. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/5955t4nq

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Tripathi, Subarna. “Improving Object Detection and Segmentation by Utilizing Context.” 2018. Thesis, University of California – San Diego. Accessed January 22, 2020. http://www.escholarship.org/uc/item/5955t4nq.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Tripathi, Subarna. “Improving Object Detection and Segmentation by Utilizing Context.” 2018. Web. 22 Jan 2020.

Vancouver:

Tripathi S. Improving Object Detection and Segmentation by Utilizing Context. [Internet] [Thesis]. University of California – San Diego; 2018. [cited 2020 Jan 22]. Available from: http://www.escholarship.org/uc/item/5955t4nq.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tripathi S. Improving Object Detection and Segmentation by Utilizing Context. [Thesis]. University of California – San Diego; 2018. Available from: http://www.escholarship.org/uc/item/5955t4nq

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Cal Poly

17. Venkatesh, Anirudh. Object Tracking in Games using Convolutional Neural Networks.

Degree: MS, Computer Science, 2018, Cal Poly

  Computer vision research has been growing rapidly over the last decade. Recent advancements in the field have been widely used in staple products across… (more)

Subjects/Keywords: Convolutional Neural Networks; YOLO; Games; CNNs; Neural Networks; Object Detection; Computer Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Venkatesh, A. (2018). Object Tracking in Games using Convolutional Neural Networks. (Masters Thesis). Cal Poly. Retrieved from https://digitalcommons.calpoly.edu/theses/1845 ; 10.15368/theses.2018.56

Chicago Manual of Style (16th Edition):

Venkatesh, Anirudh. “Object Tracking in Games using Convolutional Neural Networks.” 2018. Masters Thesis, Cal Poly. Accessed January 22, 2020. https://digitalcommons.calpoly.edu/theses/1845 ; 10.15368/theses.2018.56.

MLA Handbook (7th Edition):

Venkatesh, Anirudh. “Object Tracking in Games using Convolutional Neural Networks.” 2018. Web. 22 Jan 2020.

Vancouver:

Venkatesh A. Object Tracking in Games using Convolutional Neural Networks. [Internet] [Masters thesis]. Cal Poly; 2018. [cited 2020 Jan 22]. Available from: https://digitalcommons.calpoly.edu/theses/1845 ; 10.15368/theses.2018.56.

Council of Science Editors:

Venkatesh A. Object Tracking in Games using Convolutional Neural Networks. [Masters Thesis]. Cal Poly; 2018. Available from: https://digitalcommons.calpoly.edu/theses/1845 ; 10.15368/theses.2018.56


Tampere University

18. Khan, Umair. Prostate cancer detection using deep learning .

Degree: 2019, Tampere University

 Cancer detection is one of the principal topics of research in medical science. May it be breast, lung, brain or prostate cancer, advances are being… (more)

Subjects/Keywords: deep neural networks; multi-parametric magnetic resonance imaging (mpMRI); convolutional neural networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Khan, U. (2019). Prostate cancer detection using deep learning . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi//handle/10024/115990

Chicago Manual of Style (16th Edition):

Khan, Umair. “Prostate cancer detection using deep learning .” 2019. Masters Thesis, Tampere University. Accessed January 22, 2020. https://trepo.tuni.fi//handle/10024/115990.

MLA Handbook (7th Edition):

Khan, Umair. “Prostate cancer detection using deep learning .” 2019. Web. 22 Jan 2020.

Vancouver:

Khan U. Prostate cancer detection using deep learning . [Internet] [Masters thesis]. Tampere University; 2019. [cited 2020 Jan 22]. Available from: https://trepo.tuni.fi//handle/10024/115990.

Council of Science Editors:

Khan U. Prostate cancer detection using deep learning . [Masters Thesis]. Tampere University; 2019. Available from: https://trepo.tuni.fi//handle/10024/115990


KTH

19. Knutsson, Adam. Hand Detection and Pose Estimation using Convolutional Neural Networks.

Degree: Computer Science and Communication (CSC), 2015, KTH

This thesis examines how convolutional neural networks can applied to the problem of hand detection and hand pose estimation. Two families of convolutional neural(more)

Subjects/Keywords: machine learning; artificial neural networks; convolutional neural networks; computer vision; Computer Sciences; Datavetenskap (datalogi)

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APA (6th Edition):

Knutsson, A. (2015). Hand Detection and Pose Estimation using Convolutional Neural Networks. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174197

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Knutsson, Adam. “Hand Detection and Pose Estimation using Convolutional Neural Networks.” 2015. Thesis, KTH. Accessed January 22, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174197.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Knutsson, Adam. “Hand Detection and Pose Estimation using Convolutional Neural Networks.” 2015. Web. 22 Jan 2020.

Vancouver:

Knutsson A. Hand Detection and Pose Estimation using Convolutional Neural Networks. [Internet] [Thesis]. KTH; 2015. [cited 2020 Jan 22]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174197.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Knutsson A. Hand Detection and Pose Estimation using Convolutional Neural Networks. [Thesis]. KTH; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174197

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Colorado State University

20. Forney, Elliott Michael. Convolutional Neural Networks for EEG Signal Classification in Asynchronous Brain-Computer Interfaces.

Degree: PhD, Computer Science, 2020, Colorado State University

 Brain-Computer Interfaces (BCIs) are emerging technologies that enable users to interact with computerized devices using only voluntary changes in their mental state. BCIs have a… (more)

Subjects/Keywords: Brain-Computer Interfaces; Electroencephalography; Artificial Neural Networks; Mental Tasks; Convolutional Neural Networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Forney, E. M. (2020). Convolutional Neural Networks for EEG Signal Classification in Asynchronous Brain-Computer Interfaces. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/199806

Chicago Manual of Style (16th Edition):

Forney, Elliott Michael. “Convolutional Neural Networks for EEG Signal Classification in Asynchronous Brain-Computer Interfaces.” 2020. Doctoral Dissertation, Colorado State University. Accessed January 22, 2020. http://hdl.handle.net/10217/199806.

MLA Handbook (7th Edition):

Forney, Elliott Michael. “Convolutional Neural Networks for EEG Signal Classification in Asynchronous Brain-Computer Interfaces.” 2020. Web. 22 Jan 2020.

Vancouver:

Forney EM. Convolutional Neural Networks for EEG Signal Classification in Asynchronous Brain-Computer Interfaces. [Internet] [Doctoral dissertation]. Colorado State University; 2020. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/10217/199806.

Council of Science Editors:

Forney EM. Convolutional Neural Networks for EEG Signal Classification in Asynchronous Brain-Computer Interfaces. [Doctoral Dissertation]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/199806


Tampere University

21. Zhou, Yi. Sentiment classification with deep neural networks .

Degree: 2019, Tampere University

 Sentiment classification is an important task in Natural Language Processing (NLP) area. Deep neural networks become the mainstream method to perform the text sentiment classification… (more)

Subjects/Keywords: deep neural networks; convolutional neural network; recurrent neural network; sentiment classification; hotel reviews; TripAdvisor

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhou, Y. (2019). Sentiment classification with deep neural networks . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi//handle/10024/116148

Chicago Manual of Style (16th Edition):

Zhou, Yi. “Sentiment classification with deep neural networks .” 2019. Masters Thesis, Tampere University. Accessed January 22, 2020. https://trepo.tuni.fi//handle/10024/116148.

MLA Handbook (7th Edition):

Zhou, Yi. “Sentiment classification with deep neural networks .” 2019. Web. 22 Jan 2020.

Vancouver:

Zhou Y. Sentiment classification with deep neural networks . [Internet] [Masters thesis]. Tampere University; 2019. [cited 2020 Jan 22]. Available from: https://trepo.tuni.fi//handle/10024/116148.

Council of Science Editors:

Zhou Y. Sentiment classification with deep neural networks . [Masters Thesis]. Tampere University; 2019. Available from: https://trepo.tuni.fi//handle/10024/116148


Kennesaw State University

22. Ordonez, Pablo F. CLASSIFICATION OF IMAGES BASED ON PIXELS THAT REPRESENT A SMALL PART OF THE SCENE. A CASE APPLIED TO MICROANEURYSMS IN FUNDUS RETINA IMAGES.

Degree: MSCS, Computer Science, 2017, Kennesaw State University

Convolutional Neural Networks (CNNs), the state of the art in image classification, have proven to be as effective as an ophthalmologist, when detecting Referable… (more)

Subjects/Keywords: Machine Learning; Convolutional Neural Networks; Retina; Other Computer Engineering

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APA (6th Edition):

Ordonez, P. F. (2017). CLASSIFICATION OF IMAGES BASED ON PIXELS THAT REPRESENT A SMALL PART OF THE SCENE. A CASE APPLIED TO MICROANEURYSMS IN FUNDUS RETINA IMAGES. (Thesis). Kennesaw State University. Retrieved from https://digitalcommons.kennesaw.edu/cs_etd/9

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ordonez, Pablo F. “CLASSIFICATION OF IMAGES BASED ON PIXELS THAT REPRESENT A SMALL PART OF THE SCENE. A CASE APPLIED TO MICROANEURYSMS IN FUNDUS RETINA IMAGES.” 2017. Thesis, Kennesaw State University. Accessed January 22, 2020. https://digitalcommons.kennesaw.edu/cs_etd/9.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ordonez, Pablo F. “CLASSIFICATION OF IMAGES BASED ON PIXELS THAT REPRESENT A SMALL PART OF THE SCENE. A CASE APPLIED TO MICROANEURYSMS IN FUNDUS RETINA IMAGES.” 2017. Web. 22 Jan 2020.

Vancouver:

Ordonez PF. CLASSIFICATION OF IMAGES BASED ON PIXELS THAT REPRESENT A SMALL PART OF THE SCENE. A CASE APPLIED TO MICROANEURYSMS IN FUNDUS RETINA IMAGES. [Internet] [Thesis]. Kennesaw State University; 2017. [cited 2020 Jan 22]. Available from: https://digitalcommons.kennesaw.edu/cs_etd/9.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ordonez PF. CLASSIFICATION OF IMAGES BASED ON PIXELS THAT REPRESENT A SMALL PART OF THE SCENE. A CASE APPLIED TO MICROANEURYSMS IN FUNDUS RETINA IMAGES. [Thesis]. Kennesaw State University; 2017. Available from: https://digitalcommons.kennesaw.edu/cs_etd/9

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Illinois – Urbana-Champaign

23. Prabhu, Namrata. Identifying facial landmarks, action units and emotions using deep networks.

Degree: MS, Computer Science, 2016, University of Illinois – Urbana-Champaign

 The goal of this thesis it to use deep neural networks, specifically Convolutional Neural Networks (CNNs) to predict facial landmarks, facial action units and emotions… (more)

Subjects/Keywords: Convolutional Neural Networks (CNN); Facial Landmarks; Expressions; Action Units

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Prabhu, N. (2016). Identifying facial landmarks, action units and emotions using deep networks. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/92967

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Prabhu, Namrata. “Identifying facial landmarks, action units and emotions using deep networks.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed January 22, 2020. http://hdl.handle.net/2142/92967.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Prabhu, Namrata. “Identifying facial landmarks, action units and emotions using deep networks.” 2016. Web. 22 Jan 2020.

Vancouver:

Prabhu N. Identifying facial landmarks, action units and emotions using deep networks. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/2142/92967.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Prabhu N. Identifying facial landmarks, action units and emotions using deep networks. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/92967

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Rochester Institute of Technology

24. Chennupati, Sumanth. Hierarchical Decomposition of Large Deep Networks.

Degree: MS, Computer Engineering, 2016, Rochester Institute of Technology

  Teaching computers how to recognize people and objects from visual cues in images and videos is an interesting challenge. The computer vision and pattern… (more)

Subjects/Keywords: Convolutional neural networks; Hierarchical decomposition; Image classification; Multi layer perceptron

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APA (6th Edition):

Chennupati, S. (2016). Hierarchical Decomposition of Large Deep Networks. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9288

Chicago Manual of Style (16th Edition):

Chennupati, Sumanth. “Hierarchical Decomposition of Large Deep Networks.” 2016. Masters Thesis, Rochester Institute of Technology. Accessed January 22, 2020. https://scholarworks.rit.edu/theses/9288.

MLA Handbook (7th Edition):

Chennupati, Sumanth. “Hierarchical Decomposition of Large Deep Networks.” 2016. Web. 22 Jan 2020.

Vancouver:

Chennupati S. Hierarchical Decomposition of Large Deep Networks. [Internet] [Masters thesis]. Rochester Institute of Technology; 2016. [cited 2020 Jan 22]. Available from: https://scholarworks.rit.edu/theses/9288.

Council of Science Editors:

Chennupati S. Hierarchical Decomposition of Large Deep Networks. [Masters Thesis]. Rochester Institute of Technology; 2016. Available from: https://scholarworks.rit.edu/theses/9288


Rochester Institute of Technology

25. Thomas, Titus Pallithottathu. The Emotional Impact of Audio - Visual Stimuli.

Degree: MS, Computer Engineering, 2017, Rochester Institute of Technology

  Induced affect is the emotional effect of an object on an individual. It can be quantified through two metrics: valence and arousal. Valance quantifies… (more)

Subjects/Keywords: Affective analysis; Computer vision; Convolutional neural networks; Deep learning

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APA (6th Edition):

Thomas, T. P. (2017). The Emotional Impact of Audio - Visual Stimuli. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9557

Chicago Manual of Style (16th Edition):

Thomas, Titus Pallithottathu. “The Emotional Impact of Audio - Visual Stimuli.” 2017. Masters Thesis, Rochester Institute of Technology. Accessed January 22, 2020. https://scholarworks.rit.edu/theses/9557.

MLA Handbook (7th Edition):

Thomas, Titus Pallithottathu. “The Emotional Impact of Audio - Visual Stimuli.” 2017. Web. 22 Jan 2020.

Vancouver:

Thomas TP. The Emotional Impact of Audio - Visual Stimuli. [Internet] [Masters thesis]. Rochester Institute of Technology; 2017. [cited 2020 Jan 22]. Available from: https://scholarworks.rit.edu/theses/9557.

Council of Science Editors:

Thomas TP. The Emotional Impact of Audio - Visual Stimuli. [Masters Thesis]. Rochester Institute of Technology; 2017. Available from: https://scholarworks.rit.edu/theses/9557


University of California – San Diego

26. Merkow, Jameson Tyler. Dense Image-to-Image and Volume-to-Volume Labeling.

Degree: Electrical Engineering (Signal and Image Proc), 2017, University of California – San Diego

 This thesis presents three principled approaches to dense pixel level labeling and demonstrates their effectiveness for both segmentation and boundary detection.First, a structured decision tree… (more)

Subjects/Keywords: Computer science; Medical imaging; Boundary Detection; Convolutional Neural Networks; Segmentation

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APA (6th Edition):

Merkow, J. T. (2017). Dense Image-to-Image and Volume-to-Volume Labeling. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/2zs578mq

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Merkow, Jameson Tyler. “Dense Image-to-Image and Volume-to-Volume Labeling.” 2017. Thesis, University of California – San Diego. Accessed January 22, 2020. http://www.escholarship.org/uc/item/2zs578mq.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Merkow, Jameson Tyler. “Dense Image-to-Image and Volume-to-Volume Labeling.” 2017. Web. 22 Jan 2020.

Vancouver:

Merkow JT. Dense Image-to-Image and Volume-to-Volume Labeling. [Internet] [Thesis]. University of California – San Diego; 2017. [cited 2020 Jan 22]. Available from: http://www.escholarship.org/uc/item/2zs578mq.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Merkow JT. Dense Image-to-Image and Volume-to-Volume Labeling. [Thesis]. University of California – San Diego; 2017. Available from: http://www.escholarship.org/uc/item/2zs578mq

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of California – San Diego

27. Goyal, Ankit. Relation Extraction using Convolution Neural Networks for curation of GWAS catalog.

Degree: Computer Science, 2016, University of California – San Diego

 A crucial area of Natural Language Processing is information extraction, the study of the identification and extraction of concepts of interest ("genes", "diseases", etc.). This… (more)

Subjects/Keywords: Computer science; Artificial intelligence; Convolutional Neural Networks; GWAS; Relation Extraction

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APA (6th Edition):

Goyal, A. (2016). Relation Extraction using Convolution Neural Networks for curation of GWAS catalog. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/6b57d1gk

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Goyal, Ankit. “Relation Extraction using Convolution Neural Networks for curation of GWAS catalog.” 2016. Thesis, University of California – San Diego. Accessed January 22, 2020. http://www.escholarship.org/uc/item/6b57d1gk.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Goyal, Ankit. “Relation Extraction using Convolution Neural Networks for curation of GWAS catalog.” 2016. Web. 22 Jan 2020.

Vancouver:

Goyal A. Relation Extraction using Convolution Neural Networks for curation of GWAS catalog. [Internet] [Thesis]. University of California – San Diego; 2016. [cited 2020 Jan 22]. Available from: http://www.escholarship.org/uc/item/6b57d1gk.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Goyal A. Relation Extraction using Convolution Neural Networks for curation of GWAS catalog. [Thesis]. University of California – San Diego; 2016. Available from: http://www.escholarship.org/uc/item/6b57d1gk

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of South Carolina

28. Shi, Yang. Manufacturing Feature Recognition With 2D Convolutional Neural Networks.

Degree: MS, Mechanical Engineering, 2018, University of South Carolina

  Feature recognition is a critical sub-discipline of CAD/CAM that focuses on the design and implementation of algorithms for automated identification of manufacturing features. The… (more)

Subjects/Keywords: Mechanical Engineering; Manufacturing; Feature Recognition; D Convolutional; Neural Networks

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APA (6th Edition):

Shi, Y. (2018). Manufacturing Feature Recognition With 2D Convolutional Neural Networks. (Masters Thesis). University of South Carolina. Retrieved from https://scholarcommons.sc.edu/etd/5100

Chicago Manual of Style (16th Edition):

Shi, Yang. “Manufacturing Feature Recognition With 2D Convolutional Neural Networks.” 2018. Masters Thesis, University of South Carolina. Accessed January 22, 2020. https://scholarcommons.sc.edu/etd/5100.

MLA Handbook (7th Edition):

Shi, Yang. “Manufacturing Feature Recognition With 2D Convolutional Neural Networks.” 2018. Web. 22 Jan 2020.

Vancouver:

Shi Y. Manufacturing Feature Recognition With 2D Convolutional Neural Networks. [Internet] [Masters thesis]. University of South Carolina; 2018. [cited 2020 Jan 22]. Available from: https://scholarcommons.sc.edu/etd/5100.

Council of Science Editors:

Shi Y. Manufacturing Feature Recognition With 2D Convolutional Neural Networks. [Masters Thesis]. University of South Carolina; 2018. Available from: https://scholarcommons.sc.edu/etd/5100


Rochester Institute of Technology

29. Jain, Saloni Mahendra. Detection of Autism using Magnetic Resonance Imaging data and Graph Convolutional Neural Networks.

Degree: MS, Computer Engineering, 2018, Rochester Institute of Technology

  Autism or Autism Spectrum Disorder (ASD) is a development disability which generally begins during childhood and may last throughout the lifetime of an individual.… (more)

Subjects/Keywords: ABIDE; Adjacency convolution; Autism; Graph convolutional neural networks; Temporal

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APA (6th Edition):

Jain, S. M. (2018). Detection of Autism using Magnetic Resonance Imaging data and Graph Convolutional Neural Networks. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9972

Chicago Manual of Style (16th Edition):

Jain, Saloni Mahendra. “Detection of Autism using Magnetic Resonance Imaging data and Graph Convolutional Neural Networks.” 2018. Masters Thesis, Rochester Institute of Technology. Accessed January 22, 2020. https://scholarworks.rit.edu/theses/9972.

MLA Handbook (7th Edition):

Jain, Saloni Mahendra. “Detection of Autism using Magnetic Resonance Imaging data and Graph Convolutional Neural Networks.” 2018. Web. 22 Jan 2020.

Vancouver:

Jain SM. Detection of Autism using Magnetic Resonance Imaging data and Graph Convolutional Neural Networks. [Internet] [Masters thesis]. Rochester Institute of Technology; 2018. [cited 2020 Jan 22]. Available from: https://scholarworks.rit.edu/theses/9972.

Council of Science Editors:

Jain SM. Detection of Autism using Magnetic Resonance Imaging data and Graph Convolutional Neural Networks. [Masters Thesis]. Rochester Institute of Technology; 2018. Available from: https://scholarworks.rit.edu/theses/9972


University of Oklahoma

30. Pires de Lima, Rafael. Machine learning applications for geoscience problems.

Degree: PhD, 2019, University of Oklahoma

 Geoscientists have used machine learning for at least three decades and the applications spam many fields, from seismic processing and interpretation, to remote sensing classification,… (more)

Subjects/Keywords: convolutional neural networks; image classification; lithofacies classification; transfer learning

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APA (6th Edition):

Pires de Lima, R. (2019). Machine learning applications for geoscience problems. (Doctoral Dissertation). University of Oklahoma. Retrieved from http://hdl.handle.net/11244/322840

Chicago Manual of Style (16th Edition):

Pires de Lima, Rafael. “Machine learning applications for geoscience problems.” 2019. Doctoral Dissertation, University of Oklahoma. Accessed January 22, 2020. http://hdl.handle.net/11244/322840.

MLA Handbook (7th Edition):

Pires de Lima, Rafael. “Machine learning applications for geoscience problems.” 2019. Web. 22 Jan 2020.

Vancouver:

Pires de Lima R. Machine learning applications for geoscience problems. [Internet] [Doctoral dissertation]. University of Oklahoma; 2019. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/11244/322840.

Council of Science Editors:

Pires de Lima R. Machine learning applications for geoscience problems. [Doctoral Dissertation]. University of Oklahoma; 2019. Available from: http://hdl.handle.net/11244/322840

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