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You searched for subject:(deep neural networks). Showing records 1 – 30 of 877 total matches.

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University of Cape Town

1. Conway, Alexander. Deep neural networks for video classification in ecology.

Degree: Master Thesis, Statistical Sciences, 2020, University of Cape Town

 Analyzing large volumes of video data is a challenging and time-consuming task. Automating this process would very valuable, especially in ecological research where massive amounts… (more)

Subjects/Keywords: Deep Neural Networks; Computer Vision

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APA (6th Edition):

Conway, A. (2020). Deep neural networks for video classification in ecology. (Masters Thesis). University of Cape Town. Retrieved from http://hdl.handle.net/11427/32520

Chicago Manual of Style (16th Edition):

Conway, Alexander. “Deep neural networks for video classification in ecology.” 2020. Masters Thesis, University of Cape Town. Accessed May 07, 2021. http://hdl.handle.net/11427/32520.

MLA Handbook (7th Edition):

Conway, Alexander. “Deep neural networks for video classification in ecology.” 2020. Web. 07 May 2021.

Vancouver:

Conway A. Deep neural networks for video classification in ecology. [Internet] [Masters thesis]. University of Cape Town; 2020. [cited 2021 May 07]. Available from: http://hdl.handle.net/11427/32520.

Council of Science Editors:

Conway A. Deep neural networks for video classification in ecology. [Masters Thesis]. University of Cape Town; 2020. Available from: http://hdl.handle.net/11427/32520


Rochester Institute of Technology

2. Nooka, Sai Prasad. Fusion of Mini-Deep Nets.

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

  Image classification and object recognition are some of the most prominent problems in computer vision. The difficult nature of finding objects regardless of pose… (more)

Subjects/Keywords: Artificial intelligence; Deep neural networks; Herarchial deep networks

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APA (6th Edition):

Nooka, S. P. (2016). Fusion of Mini-Deep Nets. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9188

Chicago Manual of Style (16th Edition):

Nooka, Sai Prasad. “Fusion of Mini-Deep Nets.” 2016. Masters Thesis, Rochester Institute of Technology. Accessed May 07, 2021. https://scholarworks.rit.edu/theses/9188.

MLA Handbook (7th Edition):

Nooka, Sai Prasad. “Fusion of Mini-Deep Nets.” 2016. Web. 07 May 2021.

Vancouver:

Nooka SP. Fusion of Mini-Deep Nets. [Internet] [Masters thesis]. Rochester Institute of Technology; 2016. [cited 2021 May 07]. Available from: https://scholarworks.rit.edu/theses/9188.

Council of Science Editors:

Nooka SP. Fusion of Mini-Deep Nets. [Masters Thesis]. Rochester Institute of Technology; 2016. Available from: https://scholarworks.rit.edu/theses/9188


ETH Zürich

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

Degree: 2017, ETH Zürich

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

Subjects/Keywords: Deep Neural Networks; Event-driven sensors; Deep neural networks (DNNs); Spiking deep neural networks; Recurrent Neural Networks; Convolutional neural networks; info:eu-repo/classification/ddc/4; Data processing, computer science

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Neil, Daniel. “Deep Neural Networks and Hardware Systems for Event-driven Data.” 2017. Doctoral Dissertation, ETH Zürich. Accessed May 07, 2021. http://hdl.handle.net/20.500.11850/168865.

MLA Handbook (7th Edition):

Neil, Daniel. “Deep Neural Networks and Hardware Systems for Event-driven Data.” 2017. Web. 07 May 2021.

Vancouver:

Neil D. Deep Neural Networks and Hardware Systems for Event-driven Data. [Internet] [Doctoral dissertation]. ETH Zürich; 2017. [cited 2021 May 07]. Available from: http://hdl.handle.net/20.500.11850/168865.

Council of Science Editors:

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

4. Avelar, Pedro Henrique da Costa. Learning centrality measures with graph neural networks.

Degree: 2019, Brazil

Medidas de Centralidade são um tipo de métrica importante na Análise de Redes Sociais. Tais métricas permitem inferir qual entidade é mais central (ou informalmente,… (more)

Subjects/Keywords: Redes neurais; Grafos; Deep neural networks; recurrent neural networks; graph neural networks; graphs; centrality measures

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APA (6th Edition):

Avelar, P. H. d. C. (2019). Learning centrality measures with graph neural networks. (Masters Thesis). Brazil. Retrieved from http://hdl.handle.net/10183/204663

Chicago Manual of Style (16th Edition):

Avelar, Pedro Henrique da Costa. “Learning centrality measures with graph neural networks.” 2019. Masters Thesis, Brazil. Accessed May 07, 2021. http://hdl.handle.net/10183/204663.

MLA Handbook (7th Edition):

Avelar, Pedro Henrique da Costa. “Learning centrality measures with graph neural networks.” 2019. Web. 07 May 2021.

Vancouver:

Avelar PHdC. Learning centrality measures with graph neural networks. [Internet] [Masters thesis]. Brazil; 2019. [cited 2021 May 07]. Available from: http://hdl.handle.net/10183/204663.

Council of Science Editors:

Avelar PHdC. Learning centrality measures with graph neural networks. [Masters Thesis]. Brazil; 2019. Available from: http://hdl.handle.net/10183/204663


Oregon State University

5. Li, Xin. Don't Fool Me : Detecting Adversarial Examples in Deep Networks.

Degree: MS, Electric and Computer Engineering, 2016, Oregon State University

Deep learning has greatly improved visual recognition in recent years. However, recent research has shown that there exist many adversarial examples that can negatively impact… (more)

Subjects/Keywords: Deep Network; Neural networks (Computer science)

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APA (6th Edition):

Li, X. (2016). Don't Fool Me : Detecting Adversarial Examples in Deep Networks. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/59867

Chicago Manual of Style (16th Edition):

Li, Xin. “Don't Fool Me : Detecting Adversarial Examples in Deep Networks.” 2016. Masters Thesis, Oregon State University. Accessed May 07, 2021. http://hdl.handle.net/1957/59867.

MLA Handbook (7th Edition):

Li, Xin. “Don't Fool Me : Detecting Adversarial Examples in Deep Networks.” 2016. Web. 07 May 2021.

Vancouver:

Li X. Don't Fool Me : Detecting Adversarial Examples in Deep Networks. [Internet] [Masters thesis]. Oregon State University; 2016. [cited 2021 May 07]. Available from: http://hdl.handle.net/1957/59867.

Council of Science Editors:

Li X. Don't Fool Me : Detecting Adversarial Examples in Deep Networks. [Masters Thesis]. Oregon State University; 2016. Available from: http://hdl.handle.net/1957/59867


Vanderbilt University

6. 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 May 07, 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. 07 May 2021.

Vancouver:

Paul JS. Deep Learning for Brain Tumor Classification. [Internet] [Thesis]. Vanderbilt University; 2016. [cited 2021 May 07]. 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


University of Toronto

7. Say, Buser. Optimal Planning with Learned Neural Network Transition Models.

Degree: PhD, 2020, University of Toronto

 For many real-world automated planning problems, it is difficult to obtain a transition model that governs state evolution with complex dynamics. Fortunately, the availability of… (more)

Subjects/Keywords: Automated Planning; Deep Neural Networks; Optimization; 0800

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APA (6th Edition):

Say, B. (2020). Optimal Planning with Learned Neural Network Transition Models. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/101074

Chicago Manual of Style (16th Edition):

Say, Buser. “Optimal Planning with Learned Neural Network Transition Models.” 2020. Doctoral Dissertation, University of Toronto. Accessed May 07, 2021. http://hdl.handle.net/1807/101074.

MLA Handbook (7th Edition):

Say, Buser. “Optimal Planning with Learned Neural Network Transition Models.” 2020. Web. 07 May 2021.

Vancouver:

Say B. Optimal Planning with Learned Neural Network Transition Models. [Internet] [Doctoral dissertation]. University of Toronto; 2020. [cited 2021 May 07]. Available from: http://hdl.handle.net/1807/101074.

Council of Science Editors:

Say B. Optimal Planning with Learned Neural Network Transition Models. [Doctoral Dissertation]. University of Toronto; 2020. Available from: http://hdl.handle.net/1807/101074


University of Sydney

8. Yan, Ke. Cognition-Aware Deep Learning Models for Saliency Detection .

Degree: 2020, University of Sydney

 Saliency on an image is defined as the meaningful and attractive region corresponding to human visual perception and cognition systems. The detection of saliency is… (more)

Subjects/Keywords: saliency detection; deep neural networks; cognition

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APA (6th Edition):

Yan, K. (2020). Cognition-Aware Deep Learning Models for Saliency Detection . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/23712

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):

Yan, Ke. “Cognition-Aware Deep Learning Models for Saliency Detection .” 2020. Thesis, University of Sydney. Accessed May 07, 2021. http://hdl.handle.net/2123/23712.

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

MLA Handbook (7th Edition):

Yan, Ke. “Cognition-Aware Deep Learning Models for Saliency Detection .” 2020. Web. 07 May 2021.

Vancouver:

Yan K. Cognition-Aware Deep Learning Models for Saliency Detection . [Internet] [Thesis]. University of Sydney; 2020. [cited 2021 May 07]. Available from: http://hdl.handle.net/2123/23712.

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

Council of Science Editors:

Yan K. Cognition-Aware Deep Learning Models for Saliency Detection . [Thesis]. University of Sydney; 2020. Available from: http://hdl.handle.net/2123/23712

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


University of Sydney

9. Wasnik, Sachinkumar. Fatigue Detection in EEG Time Series Data Using Deep Learning .

Degree: 2021, University of Sydney

 Fatigue has widespread effects on the brain’s executive function, reaction time and information processing, causing loss of alertness, that affect safety, and productivity. There are… (more)

Subjects/Keywords: deep learning; EEG; neural networks; CNN; DNN

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

APA (6th Edition):

Wasnik, S. (2021). Fatigue Detection in EEG Time Series Data Using Deep Learning . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/24855

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):

Wasnik, Sachinkumar. “Fatigue Detection in EEG Time Series Data Using Deep Learning .” 2021. Thesis, University of Sydney. Accessed May 07, 2021. http://hdl.handle.net/2123/24855.

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

MLA Handbook (7th Edition):

Wasnik, Sachinkumar. “Fatigue Detection in EEG Time Series Data Using Deep Learning .” 2021. Web. 07 May 2021.

Vancouver:

Wasnik S. Fatigue Detection in EEG Time Series Data Using Deep Learning . [Internet] [Thesis]. University of Sydney; 2021. [cited 2021 May 07]. Available from: http://hdl.handle.net/2123/24855.

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

Council of Science Editors:

Wasnik S. Fatigue Detection in EEG Time Series Data Using Deep Learning . [Thesis]. University of Sydney; 2021. Available from: http://hdl.handle.net/2123/24855

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


University of Sydney

10. Wasnik, Sachinkumar. Fatigue Detection in EEG Time Series Data Using Deep Learning .

Degree: 2021, University of Sydney

 Fatigue has widespread effects on the brain’s executive function, reaction time and information processing, causing loss of alertness, that affect safety, and productivity. There are… (more)

Subjects/Keywords: deep learning; EEG; neural networks; CNN; DNN

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

APA (6th Edition):

Wasnik, S. (2021). Fatigue Detection in EEG Time Series Data Using Deep Learning . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/24917

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):

Wasnik, Sachinkumar. “Fatigue Detection in EEG Time Series Data Using Deep Learning .” 2021. Thesis, University of Sydney. Accessed May 07, 2021. http://hdl.handle.net/2123/24917.

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

MLA Handbook (7th Edition):

Wasnik, Sachinkumar. “Fatigue Detection in EEG Time Series Data Using Deep Learning .” 2021. Web. 07 May 2021.

Vancouver:

Wasnik S. Fatigue Detection in EEG Time Series Data Using Deep Learning . [Internet] [Thesis]. University of Sydney; 2021. [cited 2021 May 07]. Available from: http://hdl.handle.net/2123/24917.

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

Council of Science Editors:

Wasnik S. Fatigue Detection in EEG Time Series Data Using Deep Learning . [Thesis]. University of Sydney; 2021. Available from: http://hdl.handle.net/2123/24917

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


University of Adelaide

11. Zhang, Junjie. Deep learning for multi-label scene classification.

Degree: 2016, University of Adelaide

 Scene classification is an important topic in computer vision. For similar weather conditions, there are some obstacles for extracting features from outdoor images. In this… (more)

Subjects/Keywords: deep learning; classification; neural networks; multi-label

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APA (6th Edition):

Zhang, J. (2016). Deep learning for multi-label scene classification. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/103473

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):

Zhang, Junjie. “Deep learning for multi-label scene classification.” 2016. Thesis, University of Adelaide. Accessed May 07, 2021. http://hdl.handle.net/2440/103473.

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

MLA Handbook (7th Edition):

Zhang, Junjie. “Deep learning for multi-label scene classification.” 2016. Web. 07 May 2021.

Vancouver:

Zhang J. Deep learning for multi-label scene classification. [Internet] [Thesis]. University of Adelaide; 2016. [cited 2021 May 07]. Available from: http://hdl.handle.net/2440/103473.

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

Council of Science Editors:

Zhang J. Deep learning for multi-label scene classification. [Thesis]. University of Adelaide; 2016. Available from: http://hdl.handle.net/2440/103473

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


University of Bridgeport

12. Kaur, Tanveen. Study of deep learning approaches to face recognition and object detection YOLO .

Degree: 2019, University of Bridgeport

 In this research, I have focused on deep learning approaches to face detection and recognition and object detection and recognition. This research has mainly focused… (more)

Subjects/Keywords: Deep learning; Facial detection; Neural networks

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APA (6th Edition):

Kaur, T. (2019). Study of deep learning approaches to face recognition and object detection YOLO . (Thesis). University of Bridgeport. Retrieved from https://scholarworks.bridgeport.edu/xmlui/handle/123456789/3965

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):

Kaur, Tanveen. “Study of deep learning approaches to face recognition and object detection YOLO .” 2019. Thesis, University of Bridgeport. Accessed May 07, 2021. https://scholarworks.bridgeport.edu/xmlui/handle/123456789/3965.

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

MLA Handbook (7th Edition):

Kaur, Tanveen. “Study of deep learning approaches to face recognition and object detection YOLO .” 2019. Web. 07 May 2021.

Vancouver:

Kaur T. Study of deep learning approaches to face recognition and object detection YOLO . [Internet] [Thesis]. University of Bridgeport; 2019. [cited 2021 May 07]. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/3965.

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

Council of Science Editors:

Kaur T. Study of deep learning approaches to face recognition and object detection YOLO . [Thesis]. University of Bridgeport; 2019. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/3965

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


Rice University

13. Chen, Xu. FaceEngage: Robust Estimation of Gameplay Engagement from Real-world Front-facing Videos.

Degree: PhD, Electrical & Computer Eng., 2018, Rice University

 Measuring user cognitive engagement in interactive tasks can facilitate numerous applications toward optimizing user experience, ranging from eLearning, gaming and TV viewing. However, a significant… (more)

Subjects/Keywords: Engagement Estimation; Video Analysis; Deep Neural Networks

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APA (6th Edition):

Chen, X. (2018). FaceEngage: Robust Estimation of Gameplay Engagement from Real-world Front-facing Videos. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/105340

Chicago Manual of Style (16th Edition):

Chen, Xu. “FaceEngage: Robust Estimation of Gameplay Engagement from Real-world Front-facing Videos.” 2018. Doctoral Dissertation, Rice University. Accessed May 07, 2021. http://hdl.handle.net/1911/105340.

MLA Handbook (7th Edition):

Chen, Xu. “FaceEngage: Robust Estimation of Gameplay Engagement from Real-world Front-facing Videos.” 2018. Web. 07 May 2021.

Vancouver:

Chen X. FaceEngage: Robust Estimation of Gameplay Engagement from Real-world Front-facing Videos. [Internet] [Doctoral dissertation]. Rice University; 2018. [cited 2021 May 07]. Available from: http://hdl.handle.net/1911/105340.

Council of Science Editors:

Chen X. FaceEngage: Robust Estimation of Gameplay Engagement from Real-world Front-facing Videos. [Doctoral Dissertation]. Rice University; 2018. Available from: http://hdl.handle.net/1911/105340


Delft University of Technology

14. 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 · 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 May 07, 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. 07 May 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 May 07]. 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


Delft University of Technology

15. Sun, Wei (author). Automatic keypoint detecting of wireframe gates.

Degree: 2019, Delft University of Technology

This work applies keypoint detection method to solve gate recognition problem. Unlike regular object detection task, gate recognition problem is made difficult by the fact… (more)

Subjects/Keywords: Deep Learning; Computer Vision; Neural Networks

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APA (6th Edition):

Sun, W. (. (2019). Automatic keypoint detecting of wireframe gates. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a6d28e5b-89e6-40f3-8ef0-ab0586b6264f

Chicago Manual of Style (16th Edition):

Sun, Wei (author). “Automatic keypoint detecting of wireframe gates.” 2019. Masters Thesis, Delft University of Technology. Accessed May 07, 2021. http://resolver.tudelft.nl/uuid:a6d28e5b-89e6-40f3-8ef0-ab0586b6264f.

MLA Handbook (7th Edition):

Sun, Wei (author). “Automatic keypoint detecting of wireframe gates.” 2019. Web. 07 May 2021.

Vancouver:

Sun W(. Automatic keypoint detecting of wireframe gates. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 May 07]. Available from: http://resolver.tudelft.nl/uuid:a6d28e5b-89e6-40f3-8ef0-ab0586b6264f.

Council of Science Editors:

Sun W(. Automatic keypoint detecting of wireframe gates. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:a6d28e5b-89e6-40f3-8ef0-ab0586b6264f


University of Illinois – Urbana-Champaign

16. Basu, Sourya. Universal and succinct source coding of deep neural networks.

Degree: MS, Electrical & Computer Engr, 2020, University of Illinois – Urbana-Champaign

Deep neural networks have shown incredible performance for inference tasks in a variety of domains. Unfortunately, most current deep networks are enormous cloud-based structures that… (more)

Subjects/Keywords: Deep neural networks; Universal Source Coding

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APA (6th Edition):

Basu, S. (2020). Universal and succinct source coding of deep neural networks. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/107959

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):

Basu, Sourya. “Universal and succinct source coding of deep neural networks.” 2020. Thesis, University of Illinois – Urbana-Champaign. Accessed May 07, 2021. http://hdl.handle.net/2142/107959.

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

MLA Handbook (7th Edition):

Basu, Sourya. “Universal and succinct source coding of deep neural networks.” 2020. Web. 07 May 2021.

Vancouver:

Basu S. Universal and succinct source coding of deep neural networks. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2020. [cited 2021 May 07]. Available from: http://hdl.handle.net/2142/107959.

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

Council of Science Editors:

Basu S. Universal and succinct source coding of deep neural networks. [Thesis]. University of Illinois – Urbana-Champaign; 2020. Available from: http://hdl.handle.net/2142/107959

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


Rice University

17. Chen, Xu. FaceEngage: Robust Estimation of Gameplay Engagement from Real-world Front-facing Videos.

Degree: PhD, Engineering, 2018, Rice University

 Measuring user cognitive engagement in interactive tasks can facilitate numerous applications toward optimizing user experience, ranging from eLearning, gaming and TV viewing. However, a significant… (more)

Subjects/Keywords: Engagement Estimation; Video Analysis; Deep Neural Networks

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APA (6th Edition):

Chen, X. (2018). FaceEngage: Robust Estimation of Gameplay Engagement from Real-world Front-facing Videos. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/105339

Chicago Manual of Style (16th Edition):

Chen, Xu. “FaceEngage: Robust Estimation of Gameplay Engagement from Real-world Front-facing Videos.” 2018. Doctoral Dissertation, Rice University. Accessed May 07, 2021. http://hdl.handle.net/1911/105339.

MLA Handbook (7th Edition):

Chen, Xu. “FaceEngage: Robust Estimation of Gameplay Engagement from Real-world Front-facing Videos.” 2018. Web. 07 May 2021.

Vancouver:

Chen X. FaceEngage: Robust Estimation of Gameplay Engagement from Real-world Front-facing Videos. [Internet] [Doctoral dissertation]. Rice University; 2018. [cited 2021 May 07]. Available from: http://hdl.handle.net/1911/105339.

Council of Science Editors:

Chen X. FaceEngage: Robust Estimation of Gameplay Engagement from Real-world Front-facing Videos. [Doctoral Dissertation]. Rice University; 2018. Available from: http://hdl.handle.net/1911/105339


University of Melbourne

18. Panda, Rabindra. Large scale real-time traffic flow prediction using SCATS volume data.

Degree: 2016, University of Melbourne

 Road traffic congestion is a global issue that results in significant wastage of time and resources. Rising population, urbanisation, growing economies and affordable personal vehicles… (more)

Subjects/Keywords: traffic flow prediction; deep neural networks; LSTM

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APA (6th Edition):

Panda, R. (2016). Large scale real-time traffic flow prediction using SCATS volume data. (Masters Thesis). University of Melbourne. Retrieved from http://hdl.handle.net/11343/130024

Chicago Manual of Style (16th Edition):

Panda, Rabindra. “Large scale real-time traffic flow prediction using SCATS volume data.” 2016. Masters Thesis, University of Melbourne. Accessed May 07, 2021. http://hdl.handle.net/11343/130024.

MLA Handbook (7th Edition):

Panda, Rabindra. “Large scale real-time traffic flow prediction using SCATS volume data.” 2016. Web. 07 May 2021.

Vancouver:

Panda R. Large scale real-time traffic flow prediction using SCATS volume data. [Internet] [Masters thesis]. University of Melbourne; 2016. [cited 2021 May 07]. Available from: http://hdl.handle.net/11343/130024.

Council of Science Editors:

Panda R. Large scale real-time traffic flow prediction using SCATS volume data. [Masters Thesis]. University of Melbourne; 2016. Available from: http://hdl.handle.net/11343/130024


University of South Carolina

19. Chen, Chao. Uncertainty Estimation of Deep Neural Networks.

Degree: PhD, Computer Science and Engineering, 2018, University of South Carolina

  Normal neural networks trained with gradient descent and back-propagation have received great success in various applications. On one hand, point estimation of the network… (more)

Subjects/Keywords: Computer Sciences; Uncertainty; Estimation; Deep; Neural; Networks

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APA (6th Edition):

Chen, C. (2018). Uncertainty Estimation of Deep Neural Networks. (Doctoral Dissertation). University of South Carolina. Retrieved from https://scholarcommons.sc.edu/etd/5035

Chicago Manual of Style (16th Edition):

Chen, Chao. “Uncertainty Estimation of Deep Neural Networks.” 2018. Doctoral Dissertation, University of South Carolina. Accessed May 07, 2021. https://scholarcommons.sc.edu/etd/5035.

MLA Handbook (7th Edition):

Chen, Chao. “Uncertainty Estimation of Deep Neural Networks.” 2018. Web. 07 May 2021.

Vancouver:

Chen C. Uncertainty Estimation of Deep Neural Networks. [Internet] [Doctoral dissertation]. University of South Carolina; 2018. [cited 2021 May 07]. Available from: https://scholarcommons.sc.edu/etd/5035.

Council of Science Editors:

Chen C. Uncertainty Estimation of Deep Neural Networks. [Doctoral Dissertation]. University of South Carolina; 2018. Available from: https://scholarcommons.sc.edu/etd/5035


Virginia Tech

20. Gaopande, Meghana Laxmidhar. Exploring Accumulated Gradient-Based Quantization and Compression for Deep Neural Networks.

Degree: MS, Computer Engineering, 2020, Virginia Tech

Neural networks are being employed in many different real-world applications. By learning the complex relationship between the input data and ground-truth output data during the… (more)

Subjects/Keywords: Deep Neural Networks; Quantization; Pruning; Fixed-Point

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APA (6th Edition):

Gaopande, M. L. (2020). Exploring Accumulated Gradient-Based Quantization and Compression for Deep Neural Networks. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/98617

Chicago Manual of Style (16th Edition):

Gaopande, Meghana Laxmidhar. “Exploring Accumulated Gradient-Based Quantization and Compression for Deep Neural Networks.” 2020. Masters Thesis, Virginia Tech. Accessed May 07, 2021. http://hdl.handle.net/10919/98617.

MLA Handbook (7th Edition):

Gaopande, Meghana Laxmidhar. “Exploring Accumulated Gradient-Based Quantization and Compression for Deep Neural Networks.” 2020. Web. 07 May 2021.

Vancouver:

Gaopande ML. Exploring Accumulated Gradient-Based Quantization and Compression for Deep Neural Networks. [Internet] [Masters thesis]. Virginia Tech; 2020. [cited 2021 May 07]. Available from: http://hdl.handle.net/10919/98617.

Council of Science Editors:

Gaopande ML. Exploring Accumulated Gradient-Based Quantization and Compression for Deep Neural Networks. [Masters Thesis]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/98617


New Jersey Institute of Technology

21. Peng, Zhiqi. Characteristics of different deep neural networks and application of pre-trained model without transfer learning.

Degree: MSin Computer Science - (M.S.), Computer Science, 2017, New Jersey Institute of Technology

Deep neural networks have been successful in many areas, some of them even surpass human performances. The goal of this thesis is using data… (more)

Subjects/Keywords: Deep neural networks; Data simulations; Computer Sciences

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APA (6th Edition):

Peng, Z. (2017). Characteristics of different deep neural networks and application of pre-trained model without transfer learning. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/40

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):

Peng, Zhiqi. “Characteristics of different deep neural networks and application of pre-trained model without transfer learning.” 2017. Thesis, New Jersey Institute of Technology. Accessed May 07, 2021. https://digitalcommons.njit.edu/theses/40.

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

MLA Handbook (7th Edition):

Peng, Zhiqi. “Characteristics of different deep neural networks and application of pre-trained model without transfer learning.” 2017. Web. 07 May 2021.

Vancouver:

Peng Z. Characteristics of different deep neural networks and application of pre-trained model without transfer learning. [Internet] [Thesis]. New Jersey Institute of Technology; 2017. [cited 2021 May 07]. Available from: https://digitalcommons.njit.edu/theses/40.

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

Council of Science Editors:

Peng Z. Characteristics of different deep neural networks and application of pre-trained model without transfer learning. [Thesis]. New Jersey Institute of Technology; 2017. Available from: https://digitalcommons.njit.edu/theses/40

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


Colorado State University

22. Thivagara Sarma, Janarthanan. Pruning and acceleration of deep neural networks.

Degree: MS(M.S.), Electrical and Computer Engineering, 2020, Colorado State University

Deep neural networks are computational and memory intensive applications. Many network pruning and compression solutions has been introduced to deploy inference of large trained models… (more)

Subjects/Keywords: compression; pruning; acceleration; SIMD; deep neural networks

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APA (6th Edition):

Thivagara Sarma, J. (2020). Pruning and acceleration of deep neural networks. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/208485

Chicago Manual of Style (16th Edition):

Thivagara Sarma, Janarthanan. “Pruning and acceleration of deep neural networks.” 2020. Masters Thesis, Colorado State University. Accessed May 07, 2021. http://hdl.handle.net/10217/208485.

MLA Handbook (7th Edition):

Thivagara Sarma, Janarthanan. “Pruning and acceleration of deep neural networks.” 2020. Web. 07 May 2021.

Vancouver:

Thivagara Sarma J. Pruning and acceleration of deep neural networks. [Internet] [Masters thesis]. Colorado State University; 2020. [cited 2021 May 07]. Available from: http://hdl.handle.net/10217/208485.

Council of Science Editors:

Thivagara Sarma J. Pruning and acceleration of deep neural networks. [Masters Thesis]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/208485


University of Cambridge

23. Pearce, Tim. Uncertainty in neural networks : Bayesian ensembles, priors & prediction intervals.

Degree: PhD, 2020, University of Cambridge

 The breakout success of deep neural networks (NNs) in the 2010's marked a new era in the quest to build artificial intelligence (AI). With NNs… (more)

Subjects/Keywords: artificial intelligence; neural networks; deep learning; uncertainty

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APA (6th Edition):

Pearce, T. (2020). Uncertainty in neural networks : Bayesian ensembles, priors & prediction intervals. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.62024 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.821594

Chicago Manual of Style (16th Edition):

Pearce, Tim. “Uncertainty in neural networks : Bayesian ensembles, priors & prediction intervals.” 2020. Doctoral Dissertation, University of Cambridge. Accessed May 07, 2021. https://doi.org/10.17863/CAM.62024 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.821594.

MLA Handbook (7th Edition):

Pearce, Tim. “Uncertainty in neural networks : Bayesian ensembles, priors & prediction intervals.” 2020. Web. 07 May 2021.

Vancouver:

Pearce T. Uncertainty in neural networks : Bayesian ensembles, priors & prediction intervals. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 May 07]. Available from: https://doi.org/10.17863/CAM.62024 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.821594.

Council of Science Editors:

Pearce T. Uncertainty in neural networks : Bayesian ensembles, priors & prediction intervals. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://doi.org/10.17863/CAM.62024 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.821594


University of Cambridge

24. Pearce, Tim. Uncertainty in Neural Networks; Bayesian Ensembles, Priors & Prediction Intervals.

Degree: PhD, 2020, University of Cambridge

 The breakout success of deep neural networks (NNs) in the 2010's marked a new era in the quest to build artificial intelligence (AI). With NNs… (more)

Subjects/Keywords: artificial intelligence; neural networks; deep learning; uncertainty

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

APA (6th Edition):

Pearce, T. (2020). Uncertainty in Neural Networks; Bayesian Ensembles, Priors & Prediction Intervals. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/314918

Chicago Manual of Style (16th Edition):

Pearce, Tim. “Uncertainty in Neural Networks; Bayesian Ensembles, Priors & Prediction Intervals.” 2020. Doctoral Dissertation, University of Cambridge. Accessed May 07, 2021. https://www.repository.cam.ac.uk/handle/1810/314918.

MLA Handbook (7th Edition):

Pearce, Tim. “Uncertainty in Neural Networks; Bayesian Ensembles, Priors & Prediction Intervals.” 2020. Web. 07 May 2021.

Vancouver:

Pearce T. Uncertainty in Neural Networks; Bayesian Ensembles, Priors & Prediction Intervals. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 May 07]. Available from: https://www.repository.cam.ac.uk/handle/1810/314918.

Council of Science Editors:

Pearce T. Uncertainty in Neural Networks; Bayesian Ensembles, Priors & Prediction Intervals. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://www.repository.cam.ac.uk/handle/1810/314918


Vanderbilt University

25. 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 May 07, 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. 07 May 2021.

Vancouver:

Chen Z. Noise Suppression in Ultrasound Beamforming Using Convolutional Neural Networks. [Internet] [Thesis]. Vanderbilt University; 2019. [cited 2021 May 07]. 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 Waterloo

26. Hunsberger, Eric. Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition.

Degree: 2018, University of Waterloo

 Modern machine learning models are beginning to rival human performance on some realistic object recognition tasks, but we still lack a full understanding of how… (more)

Subjects/Keywords: learning; spiking neural networks; deep neural networks; object recognition

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APA (6th Edition):

Hunsberger, E. (2018). Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/12819

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):

Hunsberger, Eric. “Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition.” 2018. Thesis, University of Waterloo. Accessed May 07, 2021. http://hdl.handle.net/10012/12819.

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

MLA Handbook (7th Edition):

Hunsberger, Eric. “Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition.” 2018. Web. 07 May 2021.

Vancouver:

Hunsberger E. Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2021 May 07]. Available from: http://hdl.handle.net/10012/12819.

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

Council of Science Editors:

Hunsberger E. Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/12819

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


Delft University of Technology

27. 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 (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 May 07, 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. 07 May 2021.

Vancouver:

Shi X(. Interpretable Deep Visual Place Recognition. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 May 07]. 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

28. Lundborg, Sofia. Training Binary Deep Neural Networks Using Knowledge Distillation .

Degree: Chalmers tekniska högskola / Institutionen för fysik, 2020, Chalmers University of Technology

 Binary networks can be used to speed up inference time and make image analysis possible on less powerful devices. When binarizing a network the accuracy… (more)

Subjects/Keywords: deep neural networks; knowledge distillation; binary neural networks

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APA (6th Edition):

Lundborg, S. (2020). Training Binary Deep Neural Networks Using Knowledge Distillation . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/301202

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):

Lundborg, Sofia. “Training Binary Deep Neural Networks Using Knowledge Distillation .” 2020. Thesis, Chalmers University of Technology. Accessed May 07, 2021. http://hdl.handle.net/20.500.12380/301202.

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

MLA Handbook (7th Edition):

Lundborg, Sofia. “Training Binary Deep Neural Networks Using Knowledge Distillation .” 2020. Web. 07 May 2021.

Vancouver:

Lundborg S. Training Binary Deep Neural Networks Using Knowledge Distillation . [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2021 May 07]. Available from: http://hdl.handle.net/20.500.12380/301202.

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

Council of Science Editors:

Lundborg S. Training Binary Deep Neural Networks Using Knowledge Distillation . [Thesis]. Chalmers University of Technology; 2020. Available from: http://hdl.handle.net/20.500.12380/301202

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


Florida Atlantic University

29. 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 May 07, 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. 07 May 2021.

Vancouver:

Andrews WAJ. COMPARISON OF PRE-TRAINED CONVOLUTIONAL NEURAL NETWORK PERFORMANCE ON GLIOMA CLASSIFICATION. [Internet] [Masters thesis]. Florida Atlantic University; 2020. [cited 2021 May 07]. 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


Colorado State University

30. Yeluri, Sri Sagar Abhishek. Classification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters.

Degree: MS(M.S.), Computer Science, 2020, Colorado State University

 In a world where Machine Learning Algorithms in the field of Image Processing is being developed at a rapid pace, a developer needs to have… (more)

Subjects/Keywords: machine learning; Siamese-like neural networks; neural networks; deep learning

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APA (6th Edition):

Yeluri, S. S. A. (2020). Classification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/212032

Chicago Manual of Style (16th Edition):

Yeluri, Sri Sagar Abhishek. “Classification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters.” 2020. Masters Thesis, Colorado State University. Accessed May 07, 2021. http://hdl.handle.net/10217/212032.

MLA Handbook (7th Edition):

Yeluri, Sri Sagar Abhishek. “Classification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters.” 2020. Web. 07 May 2021.

Vancouver:

Yeluri SSA. Classification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters. [Internet] [Masters thesis]. Colorado State University; 2020. [cited 2021 May 07]. Available from: http://hdl.handle.net/10217/212032.

Council of Science Editors:

Yeluri SSA. Classification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters. [Masters Thesis]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/212032

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