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You searched for subject:(Convolutional Neural Networks). Showing records 1 – 30 of 296 total matches.

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

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 September 17, 2019. 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. 17 Sep 2019.

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 2019 Sep 17]. 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 Illinois – Urbana-Champaign

2. 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 (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 September 17, 2019. 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. 17 Sep 2019.

Vancouver:

Shi H. Galaxy classification with deep convolutional neural networks. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Sep 17]. 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


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 September 17, 2019. 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. 17 Sep 2019.

Vancouver:

Lin S. Analysing Generalisation Error Bounds For Convolutional Neural Networks . [Internet] [Thesis]. University of Sydney; 2018. [cited 2019 Sep 17]. 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


University of Ottawa

4. Ayoub, Issa. Multimodal Affective Computing Using Temporal Convolutional Neural Network and Deep Convolutional Neural Networks .

Degree: 2019, University of Ottawa

 Affective computing has gained significant attention from researchers in the last decade due to the wide variety of applications that can benefit from this technology.… (more)

Subjects/Keywords: Temporal Convolutional Neural Networks; Recurrent Neural Networks; Gaussian Processes; Hyperparameter Optimization; Convolutional Neural Networks

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APA (6th Edition):

Ayoub, I. (2019). Multimodal Affective Computing Using Temporal Convolutional Neural Network and Deep Convolutional Neural Networks . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/39337

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):

Ayoub, Issa. “Multimodal Affective Computing Using Temporal Convolutional Neural Network and Deep Convolutional Neural Networks .” 2019. Thesis, University of Ottawa. Accessed September 17, 2019. http://hdl.handle.net/10393/39337.

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

MLA Handbook (7th Edition):

Ayoub, Issa. “Multimodal Affective Computing Using Temporal Convolutional Neural Network and Deep Convolutional Neural Networks .” 2019. Web. 17 Sep 2019.

Vancouver:

Ayoub I. Multimodal Affective Computing Using Temporal Convolutional Neural Network and Deep Convolutional Neural Networks . [Internet] [Thesis]. University of Ottawa; 2019. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/10393/39337.

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

Council of Science Editors:

Ayoub I. Multimodal Affective Computing Using Temporal Convolutional Neural Network and Deep Convolutional Neural Networks . [Thesis]. University of Ottawa; 2019. Available from: http://hdl.handle.net/10393/39337

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


UCLA

5. 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 September 17, 2019. 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. 17 Sep 2019.

Vancouver:

Liu Y. Face Aging Using Deep Convolutional Generative Adversarial Network with Condition. [Internet] [Thesis]. UCLA; 2019. [cited 2019 Sep 17]. 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

6. 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 (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 September 17, 2019. 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. 17 Sep 2019.

Vancouver:

Olpin AJ. Convolutional Networks for Segmentation and Detection of Agricultural Mushrooms . [Internet] [Thesis]. University of Guelph; 2018. [cited 2019 Sep 17]. 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 Waterloo

7. Caterini, Anthony. A Novel Mathematical Framework for the Analysis of Neural Networks.

Degree: 2017, University of Waterloo

 Over the past decade, Deep Neural Networks (DNNs) have become very popular models for processing large amounts of data because of their successful application in… (more)

Subjects/Keywords: Neural Networks; Convolutional Neural Networks; Deep Neural Networks; Machine Learning; Recurrent Neural Networks

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APA (6th Edition):

Caterini, A. (2017). A Novel Mathematical Framework for the Analysis of Neural Networks. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/12173

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):

Caterini, Anthony. “A Novel Mathematical Framework for the Analysis of Neural Networks.” 2017. Thesis, University of Waterloo. Accessed September 17, 2019. http://hdl.handle.net/10012/12173.

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

MLA Handbook (7th Edition):

Caterini, Anthony. “A Novel Mathematical Framework for the Analysis of Neural Networks.” 2017. Web. 17 Sep 2019.

Vancouver:

Caterini A. A Novel Mathematical Framework for the Analysis of Neural Networks. [Internet] [Thesis]. University of Waterloo; 2017. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/10012/12173.

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

Council of Science Editors:

Caterini A. A Novel Mathematical Framework for the Analysis of Neural Networks. [Thesis]. University of Waterloo; 2017. Available from: http://hdl.handle.net/10012/12173

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


California State University – Sacramento

8. Deo, Sudarshan. Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization.

Degree: MS, Computer Science, 2019, California State University – Sacramento

 This project collects several experiments in Deep Learning Convolutional Neural Network for Image predictions. It makes use of Google TensorFlow and TFlearn Deep Learning libraries… (more)

Subjects/Keywords: Neural Networks; Classification; Neural networks; Convolutional neural network

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

APA (6th Edition):

Deo, S. (2019). Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/207763

Chicago Manual of Style (16th Edition):

Deo, Sudarshan. “Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization.” 2019. Masters Thesis, California State University – Sacramento. Accessed September 17, 2019. http://hdl.handle.net/10211.3/207763.

MLA Handbook (7th Edition):

Deo, Sudarshan. “Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization.” 2019. Web. 17 Sep 2019.

Vancouver:

Deo S. Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization. [Internet] [Masters thesis]. California State University – Sacramento; 2019. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/10211.3/207763.

Council of Science Editors:

Deo S. Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization. [Masters Thesis]. California State University – Sacramento; 2019. Available from: http://hdl.handle.net/10211.3/207763


University of Alberta

9. 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 (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 September 17, 2019. https://era.library.ualberta.ca/files/9g54xm59d.

MLA Handbook (7th Edition):

Hess, Andy T. “Deep Synthetic Viewpoint Prediction.” 2015. Web. 17 Sep 2019.

Vancouver:

Hess AT. Deep Synthetic Viewpoint Prediction. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2019 Sep 17]. 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


Vanderbilt University

10. 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 September 17, 2019. http://etd.library.vanderbilt.edu/available/etd-04112016-224926/ ;.

MLA Handbook (7th Edition):

Paul, Justin Stuart. “Deep Learning for Brain Tumor Classification.” 2016. Web. 17 Sep 2019.

Vancouver:

Paul JS. Deep Learning for Brain Tumor Classification. [Internet] [Masters thesis]. Vanderbilt University; 2016. [cited 2019 Sep 17]. 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/ ;


University of Waterloo

11. Xu, Yan. Sea Ice SAR Imagery Classification and Regression Based On Convolutional Neural Networks.

Degree: 2018, University of Waterloo

 Due to the global warming, there have been signficant reductions in the ice extent and ice thickness in the Arctic and marginal seas. Monitoring these… (more)

Subjects/Keywords: Sea Ice; Convolutional Neural Networks; SAR Imagery

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APA (6th Edition):

Xu, Y. (2018). Sea Ice SAR Imagery Classification and Regression Based On Convolutional Neural Networks. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/13506

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):

Xu, Yan. “Sea Ice SAR Imagery Classification and Regression Based On Convolutional Neural Networks.” 2018. Thesis, University of Waterloo. Accessed September 17, 2019. http://hdl.handle.net/10012/13506.

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

MLA Handbook (7th Edition):

Xu, Yan. “Sea Ice SAR Imagery Classification and Regression Based On Convolutional Neural Networks.” 2018. Web. 17 Sep 2019.

Vancouver:

Xu Y. Sea Ice SAR Imagery Classification and Regression Based On Convolutional Neural Networks. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/10012/13506.

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

Council of Science Editors:

Xu Y. Sea Ice SAR Imagery Classification and Regression Based On Convolutional Neural Networks. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/13506

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


ETH Zürich

12. 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 September 17, 2019. 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. 17 Sep 2019.

Vancouver:

Neil D. Deep Neural Networks and Hardware Systems for Event-driven Data. [Internet] [Doctoral dissertation]. ETH Zürich; 2017. [cited 2019 Sep 17]. 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

13. 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 http://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 September 17, 2019. http://scholar.colorado.edu/csci_gradetds/126.

MLA Handbook (7th Edition):

Dronen, Nicholas A. “Correcting Writing Errors with Convolutional Neural Networks.” 2016. Web. 17 Sep 2019.

Vancouver:

Dronen NA. Correcting Writing Errors with Convolutional Neural Networks. [Internet] [Doctoral dissertation]. University of Colorado; 2016. [cited 2019 Sep 17]. Available from: http://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: http://scholar.colorado.edu/csci_gradetds/126


Virginia Tech

14. Bendelac, Shiri. Enhanced Neural Network Training Using Selective Backpropagation and Forward Propagation.

Degree: MS, Electrical and Computer Engineering, 2018, Virginia Tech

Neural networks are making headlines every day as the tool of the future, powering artificial intelligence programs and supporting technologies never seen before. However, the… (more)

Subjects/Keywords: machine learning; neural networks; convolutional neural networks; backpropagation; forward propagation; training

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APA (6th Edition):

Bendelac, S. (2018). Enhanced Neural Network Training Using Selective Backpropagation and Forward Propagation. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83714

Chicago Manual of Style (16th Edition):

Bendelac, Shiri. “Enhanced Neural Network Training Using Selective Backpropagation and Forward Propagation.” 2018. Masters Thesis, Virginia Tech. Accessed September 17, 2019. http://hdl.handle.net/10919/83714.

MLA Handbook (7th Edition):

Bendelac, Shiri. “Enhanced Neural Network Training Using Selective Backpropagation and Forward Propagation.” 2018. Web. 17 Sep 2019.

Vancouver:

Bendelac S. Enhanced Neural Network Training Using Selective Backpropagation and Forward Propagation. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/10919/83714.

Council of Science Editors:

Bendelac S. Enhanced Neural Network Training Using Selective Backpropagation and Forward Propagation. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83714


NSYSU

15. 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 September 17, 2019. 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. 17 Sep 2019.

Vancouver:

Wang H. The Impacts of Image Contexts on Dialogue Systems. [Internet] [Thesis]. NSYSU; 2018. [cited 2019 Sep 17]. 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

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

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 September 17, 2019. 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. 17 Sep 2019.

Vancouver:

Joseph T. Joint spatial and layer attention for convolutional networks. [Internet] [Thesis]. University of Ontario Institute of Technology; 2019. [cited 2019 Sep 17]. 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


San Jose State University

17. 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 September 17, 2019. 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. 17 Sep 2019.

Vancouver:

Deshmukh KR. Image Compression Using Neural Networks. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2019 Sep 17]. 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

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

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 September 17, 2019. 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. 17 Sep 2019.

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 2019 Sep 17]. 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


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

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 September 17, 2019. 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. 17 Sep 2019.

Vancouver:

Knutsson A. Hand Detection and Pose Estimation using Convolutional Neural Networks. [Internet] [Thesis]. KTH; 2015. [cited 2019 Sep 17]. 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


University of California – San Diego

20. 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 September 17, 2019. 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. 17 Sep 2019.

Vancouver:

Tripathi S. Improving Object Detection and Segmentation by Utilizing Context. [Internet] [Thesis]. University of California – San Diego; 2018. [cited 2019 Sep 17]. 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

21. 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

Chicago Manual of Style (16th Edition):

Venkatesh, Anirudh. “Object Tracking in Games using Convolutional Neural Networks.” 2018. Masters Thesis, Cal Poly. Accessed September 17, 2019. https://digitalcommons.calpoly.edu/theses/1845.

MLA Handbook (7th Edition):

Venkatesh, Anirudh. “Object Tracking in Games using Convolutional Neural Networks.” 2018. Web. 17 Sep 2019.

Vancouver:

Venkatesh A. Object Tracking in Games using Convolutional Neural Networks. [Internet] [Masters thesis]. Cal Poly; 2018. [cited 2019 Sep 17]. Available from: https://digitalcommons.calpoly.edu/theses/1845.

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


Tampere University

22. 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 September 17, 2019. https://trepo.tuni.fi//handle/10024/115990.

MLA Handbook (7th Edition):

Khan, Umair. “Prostate cancer detection using deep learning .” 2019. Web. 17 Sep 2019.

Vancouver:

Khan U. Prostate cancer detection using deep learning . [Internet] [Masters thesis]. Tampere University; 2019. [cited 2019 Sep 17]. 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


Tampere University

23. 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 September 17, 2019. https://trepo.tuni.fi//handle/10024/116148.

MLA Handbook (7th Edition):

Zhou, Yi. “Sentiment classification with deep neural networks .” 2019. Web. 17 Sep 2019.

Vancouver:

Zhou Y. Sentiment classification with deep neural networks . [Internet] [Masters thesis]. Tampere University; 2019. [cited 2019 Sep 17]. 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

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

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 September 17, 2019. 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. 17 Sep 2019.

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 2019 Sep 17]. 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 California – San Diego

25. 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 September 17, 2019. 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. 17 Sep 2019.

Vancouver:

Merkow JT. Dense Image-to-Image and Volume-to-Volume Labeling. [Internet] [Thesis]. University of California – San Diego; 2017. [cited 2019 Sep 17]. 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

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

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 September 17, 2019. 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. 17 Sep 2019.

Vancouver:

Goyal A. Relation Extraction using Convolution Neural Networks for curation of GWAS catalog. [Internet] [Thesis]. University of California – San Diego; 2016. [cited 2019 Sep 17]. 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 Victoria

27. Dharmaretnam, Dhanush. A study of semantics across different representations of language.

Degree: Department of Computer Science, 2018, University of Victoria

 Semantics is the study of meaning and here we explore it through three major representations: brain, image and text. Researchers in the past have performed… (more)

Subjects/Keywords: Computational linguistics; Semantics; Semantics in Brain; Convolutional Neural Networks; Deep learning

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APA (6th Edition):

Dharmaretnam, D. (2018). A study of semantics across different representations of language. (Masters Thesis). University of Victoria. Retrieved from https://dspace.library.uvic.ca//handle/1828/9399

Chicago Manual of Style (16th Edition):

Dharmaretnam, Dhanush. “A study of semantics across different representations of language.” 2018. Masters Thesis, University of Victoria. Accessed September 17, 2019. https://dspace.library.uvic.ca//handle/1828/9399.

MLA Handbook (7th Edition):

Dharmaretnam, Dhanush. “A study of semantics across different representations of language.” 2018. Web. 17 Sep 2019.

Vancouver:

Dharmaretnam D. A study of semantics across different representations of language. [Internet] [Masters thesis]. University of Victoria; 2018. [cited 2019 Sep 17]. Available from: https://dspace.library.uvic.ca//handle/1828/9399.

Council of Science Editors:

Dharmaretnam D. A study of semantics across different representations of language. [Masters Thesis]. University of Victoria; 2018. Available from: https://dspace.library.uvic.ca//handle/1828/9399


University of Sydney

28. Ahn, Euijoon. Sparse Coding for Medical Image Analysis: Applications to Image Segmentation and Classification .

Degree: 2016, University of Sydney

 Medical imaging is a fundamental and invaluable tool in modern healthcare. The use of medical imaging has greatly increased, and these massive image archives provide… (more)

Subjects/Keywords: Sparsing Coding; Saliency Detection; Convolutional Neural Networks; Image Segmentation; Image Classification

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

APA (6th Edition):

Ahn, E. (2016). Sparse Coding for Medical Image Analysis: Applications to Image Segmentation and Classification . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/14971

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):

Ahn, Euijoon. “Sparse Coding for Medical Image Analysis: Applications to Image Segmentation and Classification .” 2016. Thesis, University of Sydney. Accessed September 17, 2019. http://hdl.handle.net/2123/14971.

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

MLA Handbook (7th Edition):

Ahn, Euijoon. “Sparse Coding for Medical Image Analysis: Applications to Image Segmentation and Classification .” 2016. Web. 17 Sep 2019.

Vancouver:

Ahn E. Sparse Coding for Medical Image Analysis: Applications to Image Segmentation and Classification . [Internet] [Thesis]. University of Sydney; 2016. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/2123/14971.

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

Council of Science Editors:

Ahn E. Sparse Coding for Medical Image Analysis: Applications to Image Segmentation and Classification . [Thesis]. University of Sydney; 2016. Available from: http://hdl.handle.net/2123/14971

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


University of South Carolina

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

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 September 17, 2019. https://scholarcommons.sc.edu/etd/5100.

MLA Handbook (7th Edition):

Shi, Yang. “Manufacturing Feature Recognition With 2D Convolutional Neural Networks.” 2018. Web. 17 Sep 2019.

Vancouver:

Shi Y. Manufacturing Feature Recognition With 2D Convolutional Neural Networks. [Internet] [Masters thesis]. University of South Carolina; 2018. [cited 2019 Sep 17]. 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

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

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 September 17, 2019. 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. 17 Sep 2019.

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 2019 Sep 17]. 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

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