Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for subject:(Convolutional Neural Networks). Showing records 1 – 30 of 502 total matches.

[1] [2] [3] [4] [5] … [17]

Search Limiters

Last 2 Years | English Only

Degrees

Levels

Languages

Country

▼ Search Limiters

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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 October 26, 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. 26 Oct 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 Oct 26]. 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


Delft University of Technology

2. Tetteroo, Jonathan (author). Exploring Convolutional Neural Networks on the ρ-VEX architecture.

Degree: 2018, Delft University of Technology

As machine learning algorithms play an ever increasing role in today's technology, more demands are placed on computational hardware to run these algorithms efficiently. In… (more)

Subjects/Keywords: Convolutional Neural Networks; rVEX; Streaming

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Tetteroo, J. (. (2018). Exploring Convolutional Neural Networks on the ρ-VEX architecture. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:39b1653b-cde7-419b-bcd0-8549b6e34db5

Chicago Manual of Style (16th Edition):

Tetteroo, Jonathan (author). “Exploring Convolutional Neural Networks on the ρ-VEX architecture.” 2018. Masters Thesis, Delft University of Technology. Accessed October 26, 2020. http://resolver.tudelft.nl/uuid:39b1653b-cde7-419b-bcd0-8549b6e34db5.

MLA Handbook (7th Edition):

Tetteroo, Jonathan (author). “Exploring Convolutional Neural Networks on the ρ-VEX architecture.” 2018. Web. 26 Oct 2020.

Vancouver:

Tetteroo J(. Exploring Convolutional Neural Networks on the ρ-VEX architecture. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Oct 26]. Available from: http://resolver.tudelft.nl/uuid:39b1653b-cde7-419b-bcd0-8549b6e34db5.

Council of Science Editors:

Tetteroo J(. Exploring Convolutional Neural Networks on the ρ-VEX architecture. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:39b1653b-cde7-419b-bcd0-8549b6e34db5


University of Illinois – Urbana-Champaign

3. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 October 26, 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. 26 Oct 2020.

Vancouver:

Shi H. Galaxy classification with deep convolutional neural networks. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2020 Oct 26]. 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

4. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 October 26, 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. 26 Oct 2020.

Vancouver:

Lin S. Analysing Generalisation Error Bounds For Convolutional Neural Networks . [Internet] [Thesis]. University of Sydney; 2018. [cited 2020 Oct 26]. 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 South Carolina

5. Guo, Dazhou. Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing.

Degree: PhD, Computer Science and Engineering, 2019, University of South Carolina

  Recently, semantic segmentation – assigning a categorical label to each pixel in an im- age – plays an important role in image understanding applications,… (more)

Subjects/Keywords: Computer Sciences; semantic segmentation; convolutional neural networks; Convolutional Neural Networks Backbones

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Guo, D. (2019). Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing. (Doctoral Dissertation). University of South Carolina. Retrieved from https://scholarcommons.sc.edu/etd/5600

Chicago Manual of Style (16th Edition):

Guo, Dazhou. “Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing.” 2019. Doctoral Dissertation, University of South Carolina. Accessed October 26, 2020. https://scholarcommons.sc.edu/etd/5600.

MLA Handbook (7th Edition):

Guo, Dazhou. “Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing.” 2019. Web. 26 Oct 2020.

Vancouver:

Guo D. Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing. [Internet] [Doctoral dissertation]. University of South Carolina; 2019. [cited 2020 Oct 26]. Available from: https://scholarcommons.sc.edu/etd/5600.

Council of Science Editors:

Guo D. Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing. [Doctoral Dissertation]. University of South Carolina; 2019. Available from: https://scholarcommons.sc.edu/etd/5600


UCLA

6. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 October 26, 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. 26 Oct 2020.

Vancouver:

Liu Y. Face Aging Using Deep Convolutional Generative Adversarial Network with Condition. [Internet] [Thesis]. UCLA; 2019. [cited 2020 Oct 26]. 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

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

Degree: MS, School of Computer Science, 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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. (Masters Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/13534

Chicago Manual of Style (16th Edition):

Olpin, Alexander J. “Convolutional Networks for Segmentation and Detection of Agricultural Mushrooms.” 2018. Masters Thesis, University of Guelph. Accessed October 26, 2020. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/13534.

MLA Handbook (7th Edition):

Olpin, Alexander J. “Convolutional Networks for Segmentation and Detection of Agricultural Mushrooms.” 2018. Web. 26 Oct 2020.

Vancouver:

Olpin AJ. Convolutional Networks for Segmentation and Detection of Agricultural Mushrooms. [Internet] [Masters thesis]. University of Guelph; 2018. [cited 2020 Oct 26]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/13534.

Council of Science Editors:

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


University of Alberta

8. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 October 26, 2020. https://era.library.ualberta.ca/files/9g54xm59d.

MLA Handbook (7th Edition):

Hess, Andy T. “Deep Synthetic Viewpoint Prediction.” 2015. Web. 26 Oct 2020.

Vancouver:

Hess AT. Deep Synthetic Viewpoint Prediction. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2020 Oct 26]. 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

9. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Paul, J. S. (2016). Deep Learning for Brain Tumor Classification. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12122

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):

Paul, Justin Stuart. “Deep Learning for Brain Tumor Classification.” 2016. Thesis, Vanderbilt University. Accessed October 26, 2020. http://hdl.handle.net/1803/12122.

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

MLA Handbook (7th Edition):

Paul, Justin Stuart. “Deep Learning for Brain Tumor Classification.” 2016. Web. 26 Oct 2020.

Vancouver:

Paul JS. Deep Learning for Brain Tumor Classification. [Internet] [Thesis]. Vanderbilt University; 2016. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/1803/12122.

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

Council of Science Editors:

Paul JS. Deep Learning for Brain Tumor Classification. [Thesis]. Vanderbilt University; 2016. Available from: http://hdl.handle.net/1803/12122

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


Delft University of Technology

10. Riegger, Franzi (author). Image Segmentation of the γ'-Phase in Nickelbase Superalloys utilising Deep Learning.

Degree: 2019, Delft University of Technology

 Quantitative analysis of material microstructure is a well-known method to derive chemical and physical properties of a sample. This includes the segmentation of e.g. Light… (more)

Subjects/Keywords: Convolutional Neural Networks; Image Segmentation; Deep Learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Riegger, F. (. (2019). Image Segmentation of the γ'-Phase in Nickelbase Superalloys utilising Deep Learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:62366de2-df60-4028-b541-9485cc6b7e2c

Chicago Manual of Style (16th Edition):

Riegger, Franzi (author). “Image Segmentation of the γ'-Phase in Nickelbase Superalloys utilising Deep Learning.” 2019. Masters Thesis, Delft University of Technology. Accessed October 26, 2020. http://resolver.tudelft.nl/uuid:62366de2-df60-4028-b541-9485cc6b7e2c.

MLA Handbook (7th Edition):

Riegger, Franzi (author). “Image Segmentation of the γ'-Phase in Nickelbase Superalloys utilising Deep Learning.” 2019. Web. 26 Oct 2020.

Vancouver:

Riegger F(. Image Segmentation of the γ'-Phase in Nickelbase Superalloys utilising Deep Learning. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Oct 26]. Available from: http://resolver.tudelft.nl/uuid:62366de2-df60-4028-b541-9485cc6b7e2c.

Council of Science Editors:

Riegger F(. Image Segmentation of the γ'-Phase in Nickelbase Superalloys utilising Deep Learning. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:62366de2-df60-4028-b541-9485cc6b7e2c


Virginia Tech

11. Krothapalli, Ujwal K. Regularization, Uncertainty Estimation and Out of Distribution Detection in Convolutional Neural Networks.

Degree: PhD, Computer Engineering, 2020, Virginia Tech

 Categorization is an important task in everyday life. Humans can perform the task of classifying objects effortlessly in pictures. Machines can also be trained to… (more)

Subjects/Keywords: Overconfidence; Uncertainty Estimation; Regularization; Convolutional Neural Networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Krothapalli, U. K. (2020). Regularization, Uncertainty Estimation and Out of Distribution Detection in Convolutional Neural Networks. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/99953

Chicago Manual of Style (16th Edition):

Krothapalli, Ujwal K. “Regularization, Uncertainty Estimation and Out of Distribution Detection in Convolutional Neural Networks.” 2020. Doctoral Dissertation, Virginia Tech. Accessed October 26, 2020. http://hdl.handle.net/10919/99953.

MLA Handbook (7th Edition):

Krothapalli, Ujwal K. “Regularization, Uncertainty Estimation and Out of Distribution Detection in Convolutional Neural Networks.” 2020. Web. 26 Oct 2020.

Vancouver:

Krothapalli UK. Regularization, Uncertainty Estimation and Out of Distribution Detection in Convolutional Neural Networks. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/10919/99953.

Council of Science Editors:

Krothapalli UK. Regularization, Uncertainty Estimation and Out of Distribution Detection in Convolutional Neural Networks. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/99953


Vanderbilt University

12. Chen, Zhanwen. Noise Suppression in Ultrasound Beamforming Using Convolutional Neural Networks.

Degree: MS, Computer Science, 2019, Vanderbilt University

 Medical ultrasound is a noninvasive, affordable, portable, and real-time diagnostic modality that provides a cross-sectional view of tissues. Ultrasound beamforming is a widely used approach… (more)

Subjects/Keywords: neural networks; deep learning; ultrasound; beamforming; convolutional neural networks; convolution

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Chen, Z. (2019). Noise Suppression in Ultrasound Beamforming Using Convolutional Neural Networks. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/14615

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):

Chen, Zhanwen. “Noise Suppression in Ultrasound Beamforming Using Convolutional Neural Networks.” 2019. Thesis, Vanderbilt University. Accessed October 26, 2020. http://hdl.handle.net/1803/14615.

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

MLA Handbook (7th Edition):

Chen, Zhanwen. “Noise Suppression in Ultrasound Beamforming Using Convolutional Neural Networks.” 2019. Web. 26 Oct 2020.

Vancouver:

Chen Z. Noise Suppression in Ultrasound Beamforming Using Convolutional Neural Networks. [Internet] [Thesis]. Vanderbilt University; 2019. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/1803/14615.

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

Council of Science Editors:

Chen Z. Noise Suppression in Ultrasound Beamforming Using Convolutional Neural Networks. [Thesis]. Vanderbilt University; 2019. Available from: http://hdl.handle.net/1803/14615

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


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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 October 26, 2020. https://scholar.colorado.edu/csci_gradetds/126.

MLA Handbook (7th Edition):

Dronen, Nicholas A. “Correcting Writing Errors with Convolutional Neural Networks.” 2016. Web. 26 Oct 2020.

Vancouver:

Dronen NA. Correcting Writing Errors with Convolutional Neural Networks. [Internet] [Doctoral dissertation]. University of Colorado; 2016. [cited 2020 Oct 26]. 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


Delft University of Technology

14. Shi, Xiangwei (author). Interpretable Deep Visual Place Recognition.

Degree: 2018, Delft University of Technology

 We propose a framework to interpret deep convolutional models for visual place classification. Given a deep place classification model, our proposed method produces visual explanations… (more)

Subjects/Keywords: Convolutional Neural Networks; Visual Place Recognition; Interpreting Deep Neural Networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Shi, X. (. (2018). Interpretable Deep Visual Place Recognition. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a5d18d54-eb6b-43f2-8f26-8e7c34e49486

Chicago Manual of Style (16th Edition):

Shi, Xiangwei (author). “Interpretable Deep Visual Place Recognition.” 2018. Masters Thesis, Delft University of Technology. Accessed October 26, 2020. http://resolver.tudelft.nl/uuid:a5d18d54-eb6b-43f2-8f26-8e7c34e49486.

MLA Handbook (7th Edition):

Shi, Xiangwei (author). “Interpretable Deep Visual Place Recognition.” 2018. Web. 26 Oct 2020.

Vancouver:

Shi X(. Interpretable Deep Visual Place Recognition. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Oct 26]. Available from: http://resolver.tudelft.nl/uuid:a5d18d54-eb6b-43f2-8f26-8e7c34e49486.

Council of Science Editors:

Shi X(. Interpretable Deep Visual Place Recognition. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:a5d18d54-eb6b-43f2-8f26-8e7c34e49486


Delft University of Technology

15. Blom, W.B. (author). Reinforcement Learning of Visual Features.

Degree: 2016, Delft University of Technology

The digital environment has an ever increasing amount smart programs. Programs that also get smarter every day. They help us filtering spam e-mail and they… (more)

Subjects/Keywords: reinforcement learning; visual features; neural networks; unsupervised learning; convolutional neural networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Blom, W. B. (. (2016). Reinforcement Learning of Visual Features. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:2402256c-8713-4737-9c47-b97a43558705

Chicago Manual of Style (16th Edition):

Blom, W B (author). “Reinforcement Learning of Visual Features.” 2016. Masters Thesis, Delft University of Technology. Accessed October 26, 2020. http://resolver.tudelft.nl/uuid:2402256c-8713-4737-9c47-b97a43558705.

MLA Handbook (7th Edition):

Blom, W B (author). “Reinforcement Learning of Visual Features.” 2016. Web. 26 Oct 2020.

Vancouver:

Blom WB(. Reinforcement Learning of Visual Features. [Internet] [Masters thesis]. Delft University of Technology; 2016. [cited 2020 Oct 26]. Available from: http://resolver.tudelft.nl/uuid:2402256c-8713-4737-9c47-b97a43558705.

Council of Science Editors:

Blom WB(. Reinforcement Learning of Visual Features. [Masters Thesis]. Delft University of Technology; 2016. Available from: http://resolver.tudelft.nl/uuid:2402256c-8713-4737-9c47-b97a43558705


University of Illinois – Urbana-Champaign

16. Narayanan, Ramakrishnan. Exploring image recognition: applying convoluted neural networks and learning to recognize safe cyclists.

Degree: MS, Industrial Engineering, 2017, University of Illinois – Urbana-Champaign

 Today, there is a need to focus on the mobility revolution that is currently taking place. With the advent of more intelligent data gathering, there… (more)

Subjects/Keywords: Neural networks; Convolutional neural networks; Road safety; Image recognition

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Narayanan, R. (2017). Exploring image recognition: applying convoluted neural networks and learning to recognize safe cyclists. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99141

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):

Narayanan, Ramakrishnan. “Exploring image recognition: applying convoluted neural networks and learning to recognize safe cyclists.” 2017. Thesis, University of Illinois – Urbana-Champaign. Accessed October 26, 2020. http://hdl.handle.net/2142/99141.

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

MLA Handbook (7th Edition):

Narayanan, Ramakrishnan. “Exploring image recognition: applying convoluted neural networks and learning to recognize safe cyclists.” 2017. Web. 26 Oct 2020.

Vancouver:

Narayanan R. Exploring image recognition: applying convoluted neural networks and learning to recognize safe cyclists. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2017. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/2142/99141.

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

Council of Science Editors:

Narayanan R. Exploring image recognition: applying convoluted neural networks and learning to recognize safe cyclists. [Thesis]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99141

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


Florida Atlantic University

17. Andrews, Whitney Angelica Johanna. COMPARISON OF PRE-TRAINED CONVOLUTIONAL NEURAL NETWORK PERFORMANCE ON GLIOMA CLASSIFICATION.

Degree: MS, 2020, Florida Atlantic University

Gliomas are an aggressive class of brain tumors that are associated with a better prognosis at a lower grade level. Effective differentiation and classification are… (more)

Subjects/Keywords: Gliomas; Neural networks (Computer science); Deep Learning; Convolutional neural networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Andrews, W. A. J. (2020). COMPARISON OF PRE-TRAINED CONVOLUTIONAL NEURAL NETWORK PERFORMANCE ON GLIOMA CLASSIFICATION. (Masters Thesis). Florida Atlantic University. Retrieved from http://fau.digital.flvc.org/islandora/object/fau:42587

Chicago Manual of Style (16th Edition):

Andrews, Whitney Angelica Johanna. “COMPARISON OF PRE-TRAINED CONVOLUTIONAL NEURAL NETWORK PERFORMANCE ON GLIOMA CLASSIFICATION.” 2020. Masters Thesis, Florida Atlantic University. Accessed October 26, 2020. http://fau.digital.flvc.org/islandora/object/fau:42587.

MLA Handbook (7th Edition):

Andrews, Whitney Angelica Johanna. “COMPARISON OF PRE-TRAINED CONVOLUTIONAL NEURAL NETWORK PERFORMANCE ON GLIOMA CLASSIFICATION.” 2020. Web. 26 Oct 2020.

Vancouver:

Andrews WAJ. COMPARISON OF PRE-TRAINED CONVOLUTIONAL NEURAL NETWORK PERFORMANCE ON GLIOMA CLASSIFICATION. [Internet] [Masters thesis]. Florida Atlantic University; 2020. [cited 2020 Oct 26]. Available from: http://fau.digital.flvc.org/islandora/object/fau:42587.

Council of Science Editors:

Andrews WAJ. COMPARISON OF PRE-TRAINED CONVOLUTIONAL NEURAL NETWORK PERFORMANCE ON GLIOMA CLASSIFICATION. [Masters Thesis]. Florida Atlantic University; 2020. Available from: http://fau.digital.flvc.org/islandora/object/fau:42587


NSYSU

18. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 October 26, 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. 26 Oct 2020.

Vancouver:

Wang H. The Impacts of Image Contexts on Dialogue Systems. [Internet] [Thesis]. NSYSU; 2018. [cited 2020 Oct 26]. 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

19. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 October 26, 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. 26 Oct 2020.

Vancouver:

Joseph T. Joint spatial and layer attention for convolutional networks. [Internet] [Thesis]. University of Ontario Institute of Technology; 2019. [cited 2020 Oct 26]. 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


University of Waterloo

20. Radhakrishnan, Keerthijan. Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data with Fully Convolutional Networks.

Degree: 2020, University of Waterloo

 Sea ice concentration is of great interest to ship navigators and scientists who require regional ice cover understanding. Passive microwave data and image analysis charts… (more)

Subjects/Keywords: sea ice concentration; synthetic aperture radar; convolutional neural networks; fully convolutional networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Radhakrishnan, K. (2020). Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data with Fully Convolutional Networks. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16213

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):

Radhakrishnan, Keerthijan. “Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data with Fully Convolutional Networks.” 2020. Thesis, University of Waterloo. Accessed October 26, 2020. http://hdl.handle.net/10012/16213.

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

MLA Handbook (7th Edition):

Radhakrishnan, Keerthijan. “Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data with Fully Convolutional Networks.” 2020. Web. 26 Oct 2020.

Vancouver:

Radhakrishnan K. Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data with Fully Convolutional Networks. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/10012/16213.

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

Council of Science Editors:

Radhakrishnan K. Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data with Fully Convolutional Networks. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16213

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


Delft University of Technology

21. Hoogendoorn, Jasper (author). Sequential Monte Carlo method for training Neural Networks on non-stationary time series.

Degree: 2019, Delft University of Technology

In this thesis, we study the sequential Monte Carlo method for training neural networks in the context of time series forecasting. Sequential Monte Carlo can… (more)

Subjects/Keywords: sequential Monte Carlo; Neural Networks; Time Series Forecasting; Convolutional Neural Network

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Hoogendoorn, J. (. (2019). Sequential Monte Carlo method for training Neural Networks on non-stationary time series. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:659e9fd5-d251-46fe-8455-3a17bdd4f48c

Chicago Manual of Style (16th Edition):

Hoogendoorn, Jasper (author). “Sequential Monte Carlo method for training Neural Networks on non-stationary time series.” 2019. Masters Thesis, Delft University of Technology. Accessed October 26, 2020. http://resolver.tudelft.nl/uuid:659e9fd5-d251-46fe-8455-3a17bdd4f48c.

MLA Handbook (7th Edition):

Hoogendoorn, Jasper (author). “Sequential Monte Carlo method for training Neural Networks on non-stationary time series.” 2019. Web. 26 Oct 2020.

Vancouver:

Hoogendoorn J(. Sequential Monte Carlo method for training Neural Networks on non-stationary time series. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Oct 26]. Available from: http://resolver.tudelft.nl/uuid:659e9fd5-d251-46fe-8455-3a17bdd4f48c.

Council of Science Editors:

Hoogendoorn J(. Sequential Monte Carlo method for training Neural Networks on non-stationary time series. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:659e9fd5-d251-46fe-8455-3a17bdd4f48c


Universitat Pompeu Fabra

22. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 October 26, 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. 26 Oct 2020.

Vancouver:

Pons Puig J. Deep neural networks for music and audio tagging. [Internet] [Thesis]. Universitat Pompeu Fabra; 2019. [cited 2020 Oct 26]. 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

23. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 October 26, 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. 26 Oct 2020.

Vancouver:

Deshmukh KR. Image Compression Using Neural Networks. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2020 Oct 26]. 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


Penn State University

24. Fox, Maxine Rebecca. Quantization and Adaptivity of Wavelet Scattering Networks for Classification Applications.

Degree: 2020, Penn State University

 GPR has been proposed as a solution for mine clearance and the removal of unexploded ordnance. To detect targets, convolutional neural networks (CNNs) may be… (more)

Subjects/Keywords: wavelet scattering networks; quantization; machine learning; backpropagation; convolutional neural networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Fox, M. R. (2020). Quantization and Adaptivity of Wavelet Scattering Networks for Classification Applications. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/17279mrf5256

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):

Fox, Maxine Rebecca. “Quantization and Adaptivity of Wavelet Scattering Networks for Classification Applications.” 2020. Thesis, Penn State University. Accessed October 26, 2020. https://submit-etda.libraries.psu.edu/catalog/17279mrf5256.

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

MLA Handbook (7th Edition):

Fox, Maxine Rebecca. “Quantization and Adaptivity of Wavelet Scattering Networks for Classification Applications.” 2020. Web. 26 Oct 2020.

Vancouver:

Fox MR. Quantization and Adaptivity of Wavelet Scattering Networks for Classification Applications. [Internet] [Thesis]. Penn State University; 2020. [cited 2020 Oct 26]. Available from: https://submit-etda.libraries.psu.edu/catalog/17279mrf5256.

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

Council of Science Editors:

Fox MR. Quantization and Adaptivity of Wavelet Scattering Networks for Classification Applications. [Thesis]. Penn State University; 2020. Available from: https://submit-etda.libraries.psu.edu/catalog/17279mrf5256

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


Edith Cowan University

25. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 October 26, 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. 26 Oct 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 Oct 26]. 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

26. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 October 26, 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. 26 Oct 2020.

Vancouver:

Tripathi S. Improving Object Detection and Segmentation by Utilizing Context. [Internet] [Thesis]. University of California – San Diego; 2018. [cited 2020 Oct 26]. 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


Tampere University

27. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 October 26, 2020. https://trepo.tuni.fi//handle/10024/115990.

MLA Handbook (7th Edition):

Khan, Umair. “Prostate cancer detection using deep learning .” 2019. Web. 26 Oct 2020.

Vancouver:

Khan U. Prostate cancer detection using deep learning . [Internet] [Masters thesis]. Tampere University; 2019. [cited 2020 Oct 26]. 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


Colorado State University

28. Forney, Elliott M. Convolutional neural networks for EEG signal classification in asynchronous brain-computer interfaces.

Degree: PhD, Computer Science, 2019, 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Forney, E. M. (2019). 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 M. “Convolutional neural networks for EEG signal classification in asynchronous brain-computer interfaces.” 2019. Doctoral Dissertation, Colorado State University. Accessed October 26, 2020. http://hdl.handle.net/10217/199806.

MLA Handbook (7th Edition):

Forney, Elliott M. “Convolutional neural networks for EEG signal classification in asynchronous brain-computer interfaces.” 2019. Web. 26 Oct 2020.

Vancouver:

Forney EM. Convolutional neural networks for EEG signal classification in asynchronous brain-computer interfaces. [Internet] [Doctoral dissertation]. Colorado State University; 2019. [cited 2020 Oct 26]. 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; 2019. Available from: http://hdl.handle.net/10217/199806


KTH

29. 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)

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 October 26, 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. 26 Oct 2020.

Vancouver:

Knutsson A. Hand Detection and Pose Estimation using Convolutional Neural Networks. [Internet] [Thesis]. KTH; 2015. [cited 2020 Oct 26]. 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 Ontario Institute of Technology

30. Baeenh, Mohmmed. Multi-character prediction using attention.

Degree: 2020, University of Ontario Institute of Technology

 We propose a computational attention approach to localize and classify characters in a sequence in a given image. Our approach combines spatial soft-attention with attention… (more)

Subjects/Keywords: Computational Attention; Convolutional Neural Networks; Recurrent Neural Networks; Multi-Digit Classification; CAPTCHA

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Baeenh, M. (2020). Multi-character prediction using attention. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/1132

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):

Baeenh, Mohmmed. “Multi-character prediction using attention.” 2020. Thesis, University of Ontario Institute of Technology. Accessed October 26, 2020. http://hdl.handle.net/10155/1132.

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

MLA Handbook (7th Edition):

Baeenh, Mohmmed. “Multi-character prediction using attention.” 2020. Web. 26 Oct 2020.

Vancouver:

Baeenh M. Multi-character prediction using attention. [Internet] [Thesis]. University of Ontario Institute of Technology; 2020. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/10155/1132.

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

Council of Science Editors:

Baeenh M. Multi-character prediction using attention. [Thesis]. University of Ontario Institute of Technology; 2020. Available from: http://hdl.handle.net/10155/1132

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

[1] [2] [3] [4] [5] … [17]

.