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You searched for subject:(Convolutional networks). Showing records 1 – 30 of 635 total matches.

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University of Guelph

1. 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

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

APA (6th Edition):

Olpin, A. J. (2018). Convolutional Networks for Segmentation and Detection of Agricultural Mushrooms. (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 April 22, 2021. 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. 22 Apr 2021.

Vancouver:

Olpin AJ. Convolutional Networks for Segmentation and Detection of Agricultural Mushrooms. [Internet] [Masters thesis]. University of Guelph; 2018. [cited 2021 Apr 22]. 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

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

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

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

Subjects/Keywords: Convolutional Neural Networks; Image Processing

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Bowley, Connor Ryan. “Training Convolutional Neural Networks Using An Automated Feedback Loop To Estimate The Population Of Avian Species.” 2017. Masters Thesis, University of North Dakota. Accessed April 22, 2021. https://commons.und.edu/theses/2173.

MLA Handbook (7th Edition):

Bowley, Connor Ryan. “Training Convolutional Neural Networks Using An Automated Feedback Loop To Estimate The Population Of Avian Species.” 2017. Web. 22 Apr 2021.

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 2021 Apr 22]. Available from: https://commons.und.edu/theses/2173.

Council of Science Editors:

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


Delft University of Technology

3. 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

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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 April 22, 2021. 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. 22 Apr 2021.

Vancouver:

Tetteroo J(. Exploring Convolutional Neural Networks on the ρ-VEX architecture. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Apr 22]. 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

4. 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 April 22, 2021. http://hdl.handle.net/2142/90939.

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

MLA Handbook (7th Edition):

Shi, Honghui. “Galaxy classification with deep convolutional neural networks.” 2016. Web. 22 Apr 2021.

Vancouver:

Shi H. Galaxy classification with deep convolutional neural networks. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/2142/90939.

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

Council of Science Editors:

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

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


University of Sydney

5. 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 April 22, 2021. http://hdl.handle.net/2123/20315.

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

MLA Handbook (7th Edition):

Lin, Shan. “Analysing Generalisation Error Bounds For Convolutional Neural Networks .” 2018. Web. 22 Apr 2021.

Vancouver:

Lin S. Analysing Generalisation Error Bounds For Convolutional Neural Networks . [Internet] [Thesis]. University of Sydney; 2018. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/2123/20315.

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

Council of Science Editors:

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

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


University of South Carolina

6. 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

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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 April 22, 2021. https://scholarcommons.sc.edu/etd/5600.

MLA Handbook (7th Edition):

Guo, Dazhou. “Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing.” 2019. Web. 22 Apr 2021.

Vancouver:

Guo D. Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing. [Internet] [Doctoral dissertation]. University of South Carolina; 2019. [cited 2021 Apr 22]. 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

7. 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 April 22, 2021. http://www.escholarship.org/uc/item/88j0p2nx.

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

MLA Handbook (7th Edition):

Liu, Yingzhu. “Face Aging Using Deep Convolutional Generative Adversarial Network with Condition.” 2019. Web. 22 Apr 2021.

Vancouver:

Liu Y. Face Aging Using Deep Convolutional Generative Adversarial Network with Condition. [Internet] [Thesis]. UCLA; 2019. [cited 2021 Apr 22]. Available from: http://www.escholarship.org/uc/item/88j0p2nx.

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

Council of Science Editors:

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

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


University of 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

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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 April 22, 2021. https://era.library.ualberta.ca/files/9g54xm59d.

MLA Handbook (7th Edition):

Hess, Andy T. “Deep Synthetic Viewpoint Prediction.” 2015. Web. 22 Apr 2021.

Vancouver:

Hess AT. Deep Synthetic Viewpoint Prediction. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2021 Apr 22]. Available from: https://era.library.ualberta.ca/files/9g54xm59d.

Council of Science Editors:

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


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

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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 April 22, 2021. 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. 22 Apr 2021.

Vancouver:

Paul JS. Deep Learning for Brain Tumor Classification. [Internet] [Thesis]. Vanderbilt University; 2016. [cited 2021 Apr 22]. 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

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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 April 22, 2021. 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. 22 Apr 2021.

Vancouver:

Riegger F(. Image Segmentation of the γ'-Phase in Nickelbase Superalloys utilising Deep Learning. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Apr 22]. 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

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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 April 22, 2021. 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. 22 Apr 2021.

Vancouver:

Krothapalli UK. Regularization, Uncertainty Estimation and Out of Distribution Detection in Convolutional Neural Networks. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Apr 22]. 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


University of Waterloo

12. 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

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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 April 22, 2021. 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. 22 Apr 2021.

Vancouver:

Radhakrishnan K. Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data with Fully Convolutional Networks. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Apr 22]. 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


San Jose State University

13. 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 April 22, 2021. https://doi.org/10.31979/etd.h8mt-65ct ; https://scholarworks.sjsu.edu/etd_projects/666.

MLA Handbook (7th Edition):

Deshmukh, Kunal Rajan. “Image Compression Using Neural Networks.” 2019. Web. 22 Apr 2021.

Vancouver:

Deshmukh KR. Image Compression Using Neural Networks. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2021 Apr 22]. Available from: https://doi.org/10.31979/etd.h8mt-65ct ; https://scholarworks.sjsu.edu/etd_projects/666.

Council of Science Editors:

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


Vanderbilt University

14. 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

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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 April 22, 2021. 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. 22 Apr 2021.

Vancouver:

Chen Z. Noise Suppression in Ultrasound Beamforming Using Convolutional Neural Networks. [Internet] [Thesis]. Vanderbilt University; 2019. [cited 2021 Apr 22]. 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


Penn State University

15. 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

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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 April 22, 2021. 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. 22 Apr 2021.

Vancouver:

Fox MR. Quantization and Adaptivity of Wavelet Scattering Networks for Classification Applications. [Internet] [Thesis]. Penn State University; 2020. [cited 2021 Apr 22]. 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


University of Colorado

16. 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 · 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 April 22, 2021. https://scholar.colorado.edu/csci_gradetds/126.

MLA Handbook (7th Edition):

Dronen, Nicholas A. “Correcting Writing Errors with Convolutional Neural Networks.” 2016. Web. 22 Apr 2021.

Vancouver:

Dronen NA. Correcting Writing Errors with Convolutional Neural Networks. [Internet] [Doctoral dissertation]. University of Colorado; 2016. [cited 2021 Apr 22]. Available from: https://scholar.colorado.edu/csci_gradetds/126.

Council of Science Editors:

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


University of Illinois – Chicago

17. Biradar, Sachin G. User-Centric Adversarial Perturbations to Protect Privacy in Social Networks.

Degree: 2019, University of Illinois – Chicago

 Social media has completely pervaded our lives in the past few years with increased accessibility to the internet. They have drastically changed the way people… (more)

Subjects/Keywords: Privacy; Social Networks; Adversarial Attacks; Fooling Algorithm; Graph Convolutional Networks

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APA (6th Edition):

Biradar, S. G. (2019). User-Centric Adversarial Perturbations to Protect Privacy in Social Networks. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/23723

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):

Biradar, Sachin G. “User-Centric Adversarial Perturbations to Protect Privacy in Social Networks.” 2019. Thesis, University of Illinois – Chicago. Accessed April 22, 2021. http://hdl.handle.net/10027/23723.

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

MLA Handbook (7th Edition):

Biradar, Sachin G. “User-Centric Adversarial Perturbations to Protect Privacy in Social Networks.” 2019. Web. 22 Apr 2021.

Vancouver:

Biradar SG. User-Centric Adversarial Perturbations to Protect Privacy in Social Networks. [Internet] [Thesis]. University of Illinois – Chicago; 2019. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/10027/23723.

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

Council of Science Editors:

Biradar SG. User-Centric Adversarial Perturbations to Protect Privacy in Social Networks. [Thesis]. University of Illinois – Chicago; 2019. Available from: http://hdl.handle.net/10027/23723

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


Delft University of Technology

18. 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

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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 April 22, 2021. http://resolver.tudelft.nl/uuid:a5d18d54-eb6b-43f2-8f26-8e7c34e49486.

MLA Handbook (7th Edition):

Shi, Xiangwei (author). “Interpretable Deep Visual Place Recognition.” 2018. Web. 22 Apr 2021.

Vancouver:

Shi X(. Interpretable Deep Visual Place Recognition. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Apr 22]. 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

19. 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

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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 April 22, 2021. 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. 22 Apr 2021.

Vancouver:

Blom WB(. Reinforcement Learning of Visual Features. [Internet] [Masters thesis]. Delft University of Technology; 2016. [cited 2021 Apr 22]. 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

20. 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

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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 April 22, 2021. 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. 22 Apr 2021.

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 2021 Apr 22]. 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

21. 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

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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 April 22, 2021. 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. 22 Apr 2021.

Vancouver:

Andrews WAJ. COMPARISON OF PRE-TRAINED CONVOLUTIONAL NEURAL NETWORK PERFORMANCE ON GLIOMA CLASSIFICATION. [Internet] [Masters thesis]. Florida Atlantic University; 2020. [cited 2021 Apr 22]. 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


King Abdullah University of Science and Technology

22. Xiong, Chenxin. Deep GCNs with Random Partition and Generalized Aggregator.

Degree: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 2020, King Abdullah University of Science and Technology

 Graph Convolutional Networks (GCNs) draws significant attention due to its power of representation learning on graphs. Recent works developed frameworks to train deep GCNs. Such… (more)

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

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APA (6th Edition):

Xiong, C. (2020). Deep GCNs with Random Partition and Generalized Aggregator. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/666216

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):

Xiong, Chenxin. “Deep GCNs with Random Partition and Generalized Aggregator.” 2020. Thesis, King Abdullah University of Science and Technology. Accessed April 22, 2021. http://hdl.handle.net/10754/666216.

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

MLA Handbook (7th Edition):

Xiong, Chenxin. “Deep GCNs with Random Partition and Generalized Aggregator.” 2020. Web. 22 Apr 2021.

Vancouver:

Xiong C. Deep GCNs with Random Partition and Generalized Aggregator. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2020. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/10754/666216.

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

Council of Science Editors:

Xiong C. Deep GCNs with Random Partition and Generalized Aggregator. [Thesis]. King Abdullah University of Science and Technology; 2020. Available from: http://hdl.handle.net/10754/666216

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


NSYSU

23. 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 April 22, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0616118-181354.

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

MLA Handbook (7th Edition):

Wang, Hao-Yi. “The Impacts of Image Contexts on Dialogue Systems.” 2018. Web. 22 Apr 2021.

Vancouver:

Wang H. The Impacts of Image Contexts on Dialogue Systems. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 Apr 22]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0616118-181354.

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

Council of Science Editors:

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

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


University of Ontario Institute of Technology

24. 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 April 22, 2021. http://hdl.handle.net/10155/1061.

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

MLA Handbook (7th Edition):

Joseph, Tony. “Joint spatial and layer attention for convolutional networks.” 2019. Web. 22 Apr 2021.

Vancouver:

Joseph T. Joint spatial and layer attention for convolutional networks. [Internet] [Thesis]. University of Ontario Institute of Technology; 2019. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/10155/1061.

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

Council of Science Editors:

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

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


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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Caldera, Shehan. “Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot.” 2019. Thesis, Edith Cowan University. Accessed April 22, 2021. https://ro.ecu.edu.au/theses/2170.

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

MLA Handbook (7th Edition):

Caldera, Shehan. “Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot.” 2019. Web. 22 Apr 2021.

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 2021 Apr 22]. Available from: https://ro.ecu.edu.au/theses/2170.

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

Council of Science Editors:

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

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


KTH

26. Wynen, Daan. Convolutional Kernel Networks for Action Recognition in Videos.

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

  While convolutional neural networks (CNNs) have taken the lead for many learning tasks, action recognition in videos has yet to see this jump in… (more)

Subjects/Keywords: Convolutional Kernel Networks; Convolutional Neural Networks; Kernel Methods; Action Recognition; Computer Vision; Video; Computer Sciences; Datavetenskap (datalogi)

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

APA (6th Edition):

Wynen, D. (2015). Convolutional Kernel Networks for Action Recognition in Videos. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175797

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):

Wynen, Daan. “Convolutional Kernel Networks for Action Recognition in Videos.” 2015. Thesis, KTH. Accessed April 22, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175797.

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

MLA Handbook (7th Edition):

Wynen, Daan. “Convolutional Kernel Networks for Action Recognition in Videos.” 2015. Web. 22 Apr 2021.

Vancouver:

Wynen D. Convolutional Kernel Networks for Action Recognition in Videos. [Internet] [Thesis]. KTH; 2015. [cited 2021 Apr 22]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175797.

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

Council of Science Editors:

Wynen D. Convolutional Kernel Networks for Action Recognition in Videos. [Thesis]. KTH; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175797

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


Universitat Pompeu Fabra

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

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

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Pons Puig, Jordi. “Deep neural networks for music and audio tagging.” 2019. Thesis, Universitat Pompeu Fabra. Accessed April 22, 2021. http://hdl.handle.net/10803/668036.

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

MLA Handbook (7th Edition):

Pons Puig, Jordi. “Deep neural networks for music and audio tagging.” 2019. Web. 22 Apr 2021.

Vancouver:

Pons Puig J. Deep neural networks for music and audio tagging. [Internet] [Thesis]. Universitat Pompeu Fabra; 2019. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/10803/668036.

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

Council of Science Editors:

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

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


University of California – San Diego

28. 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 April 22, 2021. http://www.escholarship.org/uc/item/2zs578mq.

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

MLA Handbook (7th Edition):

Merkow, Jameson Tyler. “Dense Image-to-Image and Volume-to-Volume Labeling.” 2017. Web. 22 Apr 2021.

Vancouver:

Merkow JT. Dense Image-to-Image and Volume-to-Volume Labeling. [Internet] [Thesis]. University of California – San Diego; 2017. [cited 2021 Apr 22]. Available from: http://www.escholarship.org/uc/item/2zs578mq.

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

Council of Science Editors:

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

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


University of California – San Diego

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

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

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Goyal, Ankit. “Relation Extraction using Convolution Neural Networks for curation of GWAS catalog.” 2016. Thesis, University of California – San Diego. Accessed April 22, 2021. http://www.escholarship.org/uc/item/6b57d1gk.

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

MLA Handbook (7th Edition):

Goyal, Ankit. “Relation Extraction using Convolution Neural Networks for curation of GWAS catalog.” 2016. Web. 22 Apr 2021.

Vancouver:

Goyal A. Relation Extraction using Convolution Neural Networks for curation of GWAS catalog. [Internet] [Thesis]. University of California – San Diego; 2016. [cited 2021 Apr 22]. Available from: http://www.escholarship.org/uc/item/6b57d1gk.

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

Council of Science Editors:

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

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


Mississippi State University

30. Reza, Tasmia. Object detection using feature extraction and deep learning for advanced driver assistance systems.

Degree: MS, Electrical and Computer Engineering, 2018, Mississippi State University

 A comparison of performance between tradition support vector machine (SVM), single kernel, multiple kernel learning (MKL), and modern deep learning (DL) classifiers are observed in… (more)

Subjects/Keywords: Advanced Driver Assistance Systems; Support Vector Machine; LiDAR; Convolutional Neural Networks

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

APA (6th Edition):

Reza, T. (2018). Object detection using feature extraction and deep learning for advanced driver assistance systems. (Masters Thesis). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-06182018-140852/ ;

Chicago Manual of Style (16th Edition):

Reza, Tasmia. “Object detection using feature extraction and deep learning for advanced driver assistance systems.” 2018. Masters Thesis, Mississippi State University. Accessed April 22, 2021. http://sun.library.msstate.edu/ETD-db/theses/available/etd-06182018-140852/ ;.

MLA Handbook (7th Edition):

Reza, Tasmia. “Object detection using feature extraction and deep learning for advanced driver assistance systems.” 2018. Web. 22 Apr 2021.

Vancouver:

Reza T. Object detection using feature extraction and deep learning for advanced driver assistance systems. [Internet] [Masters thesis]. Mississippi State University; 2018. [cited 2021 Apr 22]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-06182018-140852/ ;.

Council of Science Editors:

Reza T. Object detection using feature extraction and deep learning for advanced driver assistance systems. [Masters Thesis]. Mississippi State University; 2018. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-06182018-140852/ ;

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