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You searched for subject:(Convolutional neural network). Showing records 1 – 30 of 203 total matches.

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California State University – Sacramento

1. Rawlani, Bhagyashree. Distracted driver detection using Capsule Network.

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

Convolutional neural networks are generally assumed to be the best neural networks for classifying images. In November 2017, Geoffrey Hinton et al. introduced another neural(more)

Subjects/Keywords: Convolutional neural network

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APA (6th Edition):

Rawlani, B. (2019). Distracted driver detection using Capsule Network. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/207659

Chicago Manual of Style (16th Edition):

Rawlani, Bhagyashree. “Distracted driver detection using Capsule Network.” 2019. Masters Thesis, California State University – Sacramento. Accessed May 23, 2019. http://hdl.handle.net/10211.3/207659.

MLA Handbook (7th Edition):

Rawlani, Bhagyashree. “Distracted driver detection using Capsule Network.” 2019. Web. 23 May 2019.

Vancouver:

Rawlani B. Distracted driver detection using Capsule Network. [Internet] [Masters thesis]. California State University – Sacramento; 2019. [cited 2019 May 23]. Available from: http://hdl.handle.net/10211.3/207659.

Council of Science Editors:

Rawlani B. Distracted driver detection using Capsule Network. [Masters Thesis]. California State University – Sacramento; 2019. Available from: http://hdl.handle.net/10211.3/207659


University of Saskatchewan

2. Wang, Yi 1989-. Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography.

Degree: 2018, University of Saskatchewan

 Without performing biopsy that could lead physical damages to nerves and vessels, Computerized Tomography (CT) is widely used to diagnose the lung cancer due to… (more)

Subjects/Keywords: convolutional neural network; pulmonary nodule

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APA (6th Edition):

Wang, Y. 1. (2018). Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/10844

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, Yi 1989-. “Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography.” 2018. Thesis, University of Saskatchewan. Accessed May 23, 2019. http://hdl.handle.net/10388/10844.

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

MLA Handbook (7th Edition):

Wang, Yi 1989-. “Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography.” 2018. Web. 23 May 2019.

Vancouver:

Wang Y1. Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography. [Internet] [Thesis]. University of Saskatchewan; 2018. [cited 2019 May 23]. Available from: http://hdl.handle.net/10388/10844.

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

Council of Science Editors:

Wang Y1. Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography. [Thesis]. University of Saskatchewan; 2018. Available from: http://hdl.handle.net/10388/10844

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


University of Illinois – Urbana-Champaign

3. Yeh, Raymond Alexander. Stable and symmetric convolutional neural network.

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

 First we present a proof that convolutional neural networks (CNNs) with max-norm regularization, max-pooling, and Relu non-linearity are stable to additive noise. Second, we explore… (more)

Subjects/Keywords: convolutional neural network; deep learning

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APA (6th Edition):

Yeh, R. A. (2016). Stable and symmetric convolutional neural network. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/92687

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):

Yeh, Raymond Alexander. “Stable and symmetric convolutional neural network.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed May 23, 2019. http://hdl.handle.net/2142/92687.

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

MLA Handbook (7th Edition):

Yeh, Raymond Alexander. “Stable and symmetric convolutional neural network.” 2016. Web. 23 May 2019.

Vancouver:

Yeh RA. Stable and symmetric convolutional neural network. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 May 23]. Available from: http://hdl.handle.net/2142/92687.

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

Council of Science Editors:

Yeh RA. Stable and symmetric convolutional neural network. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/92687

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


NSYSU

4. WU, JHE-WEI. Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy.

Degree: Master, Information Management, 2017, NSYSU

 The rapid increase of computing power, along with the improvement of software capabilities has made artificial intelligence a new trend. Deep learning recently becomes the… (more)

Subjects/Keywords: Convolutional Neural Network; Artificial Intelligence; Stock investment

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APA (6th Edition):

WU, J. (2017). Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-145700

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):

WU, JHE-WEI. “Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy.” 2017. Thesis, NSYSU. Accessed May 23, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-145700.

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

MLA Handbook (7th Edition):

WU, JHE-WEI. “Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy.” 2017. Web. 23 May 2019.

Vancouver:

WU J. Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy. [Internet] [Thesis]. NSYSU; 2017. [cited 2019 May 23]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-145700.

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

Council of Science Editors:

WU J. Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-145700

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


University of Illinois – Urbana-Champaign

5. Liu, Xianming. Feedback convolutional neural network in applications of computer vision.

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

 With the development of deep neural networks, especially convolutional neural networks, computer vision tasks rely on training data to an unprecedented extent. As the network(more)

Subjects/Keywords: Convolutional Neural Network; Feedback; Computer Vision

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

APA (6th Edition):

Liu, X. (2016). Feedback convolutional neural network in applications of computer vision. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/95471

Chicago Manual of Style (16th Edition):

Liu, Xianming. “Feedback convolutional neural network in applications of computer vision.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed May 23, 2019. http://hdl.handle.net/2142/95471.

MLA Handbook (7th Edition):

Liu, Xianming. “Feedback convolutional neural network in applications of computer vision.” 2016. Web. 23 May 2019.

Vancouver:

Liu X. Feedback convolutional neural network in applications of computer vision. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 May 23]. Available from: http://hdl.handle.net/2142/95471.

Council of Science Editors:

Liu X. Feedback convolutional neural network in applications of computer vision. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/95471


Mid Sweden University

6. Wang, Xutao. Chinese Text Classification Based On Deep Learning.

Degree: Information Systems and Technology, 2018, Mid Sweden University

  Text classification has always been a concern in area of natural language processing, especially nowadays the data are getting massive due to the development… (more)

Subjects/Keywords: Text classification; Recurrent neural network; Convolutional neural network; Computer Systems; Datorsystem

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

APA (6th Edition):

Wang, X. (2018). Chinese Text Classification Based On Deep Learning. (Thesis). Mid Sweden University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322

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, Xutao. “Chinese Text Classification Based On Deep Learning.” 2018. Thesis, Mid Sweden University. Accessed May 23, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322.

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

MLA Handbook (7th Edition):

Wang, Xutao. “Chinese Text Classification Based On Deep Learning.” 2018. Web. 23 May 2019.

Vancouver:

Wang X. Chinese Text Classification Based On Deep Learning. [Internet] [Thesis]. Mid Sweden University; 2018. [cited 2019 May 23]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322.

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

Council of Science Editors:

Wang X. Chinese Text Classification Based On Deep Learning. [Thesis]. Mid Sweden University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322

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

7. Sudak, Bartosh. Supervised Bug Reports Classification for More Accurate Software Defect Localization .

Degree: 2017, California State University – San Marcos

 In software development, software teams receive bug reports that describe unintended performance of the software products frequently. When a new bug report is received, software… (more)

Subjects/Keywords: Neural Network; Convolutional Neural Network; Text Similarity; Bug Finding; Defect Localization

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APA (6th Edition):

Sudak, B. (2017). Supervised Bug Reports Classification for More Accurate Software Defect Localization . (Thesis). California State University – San Marcos. Retrieved from http://hdl.handle.net/10211.3/194690

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):

Sudak, Bartosh. “Supervised Bug Reports Classification for More Accurate Software Defect Localization .” 2017. Thesis, California State University – San Marcos. Accessed May 23, 2019. http://hdl.handle.net/10211.3/194690.

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

MLA Handbook (7th Edition):

Sudak, Bartosh. “Supervised Bug Reports Classification for More Accurate Software Defect Localization .” 2017. Web. 23 May 2019.

Vancouver:

Sudak B. Supervised Bug Reports Classification for More Accurate Software Defect Localization . [Internet] [Thesis]. California State University – San Marcos; 2017. [cited 2019 May 23]. Available from: http://hdl.handle.net/10211.3/194690.

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

Council of Science Editors:

Sudak B. Supervised Bug Reports Classification for More Accurate Software Defect Localization . [Thesis]. California State University – San Marcos; 2017. Available from: http://hdl.handle.net/10211.3/194690

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


Mid Sweden University

8. Guan, Xiao. Deterministic and Flexible Parallel Latent Feature Models Learning Framework for Probabilistic Knowledge Graph.

Degree: Information Systems and Technology, 2018, Mid Sweden University

  Knowledge Graph is a rising topic in the field of Artificial Intelligence. As the current trend of knowledge representation, Knowledge graph research is utilizing… (more)

Subjects/Keywords: Text classification; Recurrent neural network; Convolutional neural network; Computer Systems; Datorsystem

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APA (6th Edition):

Guan, X. (2018). Deterministic and Flexible Parallel Latent Feature Models Learning Framework for Probabilistic Knowledge Graph. (Thesis). Mid Sweden University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35788

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):

Guan, Xiao. “Deterministic and Flexible Parallel Latent Feature Models Learning Framework for Probabilistic Knowledge Graph.” 2018. Thesis, Mid Sweden University. Accessed May 23, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35788.

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

MLA Handbook (7th Edition):

Guan, Xiao. “Deterministic and Flexible Parallel Latent Feature Models Learning Framework for Probabilistic Knowledge Graph.” 2018. Web. 23 May 2019.

Vancouver:

Guan X. Deterministic and Flexible Parallel Latent Feature Models Learning Framework for Probabilistic Knowledge Graph. [Internet] [Thesis]. Mid Sweden University; 2018. [cited 2019 May 23]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35788.

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

Council of Science Editors:

Guan X. Deterministic and Flexible Parallel Latent Feature Models Learning Framework for Probabilistic Knowledge Graph. [Thesis]. Mid Sweden University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35788

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


NSYSU

9. Liu, Jia-Hong. Portfolio Investment Based on Neural Networks.

Degree: Master, Computer Science and Engineering, 2018, NSYSU

 In this thesis, we combine the trading signals generated by the gen expression programming (GEP) method of Lee et al. and the portfolio generated by… (more)

Subjects/Keywords: neural network; stock investment; gene expression programming; convolutional neural network; portfolio

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APA (6th Edition):

Liu, J. (2018). Portfolio Investment Based on Neural Networks. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621118-142449

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, Jia-Hong. “Portfolio Investment Based on Neural Networks.” 2018. Thesis, NSYSU. Accessed May 23, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621118-142449.

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

MLA Handbook (7th Edition):

Liu, Jia-Hong. “Portfolio Investment Based on Neural Networks.” 2018. Web. 23 May 2019.

Vancouver:

Liu J. Portfolio Investment Based on Neural Networks. [Internet] [Thesis]. NSYSU; 2018. [cited 2019 May 23]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621118-142449.

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

Council of Science Editors:

Liu J. Portfolio Investment Based on Neural Networks. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621118-142449

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


Rochester Institute of Technology

10. Nguyen, Thang Huy. Automatic Video Captioning using Deep Neural Network.

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

  Video understanding has become increasingly important as surveillance, social, and informational videos weave themselves into our everyday lives. Video captioning offers a simple way… (more)

Subjects/Keywords: Convolutional neural network; Deep learning; Recurrent neural network; Video captioning

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APA (6th Edition):

Nguyen, T. H. (2017). Automatic Video Captioning using Deep Neural Network. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9516

Chicago Manual of Style (16th Edition):

Nguyen, Thang Huy. “Automatic Video Captioning using Deep Neural Network.” 2017. Masters Thesis, Rochester Institute of Technology. Accessed May 23, 2019. https://scholarworks.rit.edu/theses/9516.

MLA Handbook (7th Edition):

Nguyen, Thang Huy. “Automatic Video Captioning using Deep Neural Network.” 2017. Web. 23 May 2019.

Vancouver:

Nguyen TH. Automatic Video Captioning using Deep Neural Network. [Internet] [Masters thesis]. Rochester Institute of Technology; 2017. [cited 2019 May 23]. Available from: https://scholarworks.rit.edu/theses/9516.

Council of Science Editors:

Nguyen TH. Automatic Video Captioning using Deep Neural Network. [Masters Thesis]. Rochester Institute of Technology; 2017. Available from: https://scholarworks.rit.edu/theses/9516


California State University – Sacramento

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

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

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

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

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

APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Deo, Sudarshan. “Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization.” 2019. Masters Thesis, California State University – Sacramento. Accessed May 23, 2019. http://hdl.handle.net/10211.3/207763.

MLA Handbook (7th Edition):

Deo, Sudarshan. “Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization.” 2019. Web. 23 May 2019.

Vancouver:

Deo S. Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization. [Internet] [Masters thesis]. California State University – Sacramento; 2019. [cited 2019 May 23]. Available from: http://hdl.handle.net/10211.3/207763.

Council of Science Editors:

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


University of Illinois – Urbana-Champaign

12. Yan, Zhicheng. Image recognition, semantic segmentation and photo adjustment using deep neural networks.

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

 Deep Neural Networks (DNNs) have proven to be effective models for solving various problems in computer vision. Multi-Layer Perceptron Networks, Convolutional Neural Networks and Recurrent… (more)

Subjects/Keywords: Deep Neural Network; Image Recognition; Semantic Segmentation; Photo Adjustment; Convolutional Neural Network; Recurrent Neural Network; Multi-Layer Perceptron Network

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

APA (6th Edition):

Yan, Z. (2016). Image recognition, semantic segmentation and photo adjustment using deep neural networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90724

Chicago Manual of Style (16th Edition):

Yan, Zhicheng. “Image recognition, semantic segmentation and photo adjustment using deep neural networks.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed May 23, 2019. http://hdl.handle.net/2142/90724.

MLA Handbook (7th Edition):

Yan, Zhicheng. “Image recognition, semantic segmentation and photo adjustment using deep neural networks.” 2016. Web. 23 May 2019.

Vancouver:

Yan Z. Image recognition, semantic segmentation and photo adjustment using deep neural networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 May 23]. Available from: http://hdl.handle.net/2142/90724.

Council of Science Editors:

Yan Z. Image recognition, semantic segmentation and photo adjustment using deep neural networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90724


NSYSU

13. Wu, Tung-Han. Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition.

Degree: Master, Computer Science and Engineering, 2017, NSYSU

 In this paper, we combine the deep neural network De-noising Auto Encoder and Gaussian Mixture Model to implement an automatic speech recognition system in the… (more)

Subjects/Keywords: speech recognition; denoising auto encoder; convolutional neural network; deep learning; fully connected neural network

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APA (6th Edition):

Wu, T. (2017). Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558

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):

Wu, Tung-Han. “Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition.” 2017. Thesis, NSYSU. Accessed May 23, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558.

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

MLA Handbook (7th Edition):

Wu, Tung-Han. “Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition.” 2017. Web. 23 May 2019.

Vancouver:

Wu T. Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition. [Internet] [Thesis]. NSYSU; 2017. [cited 2019 May 23]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558.

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

Council of Science Editors:

Wu T. Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558

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


Linköping University

14. Keisala, Simon. Designing an Artificial Neural Network for state evaluation in Arimaa : Using a Convolutional Neural Network.

Degree: Artificial Intelligence and Integrated Computer Systems, 2017, Linköping University

  Agents being able to play board games such as Tic Tac Toe, Chess, Go and Arimaa has been, and still is, a major difficulty… (more)

Subjects/Keywords: neural network; machine learning; arimaa; evaluation; reinforcement learning; convolutional neural network; Computer Sciences; Datavetenskap (datalogi)

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APA (6th Edition):

Keisala, S. (2017). Designing an Artificial Neural Network for state evaluation in Arimaa : Using a Convolutional Neural Network. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143188

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):

Keisala, Simon. “Designing an Artificial Neural Network for state evaluation in Arimaa : Using a Convolutional Neural Network.” 2017. Thesis, Linköping University. Accessed May 23, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143188.

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

MLA Handbook (7th Edition):

Keisala, Simon. “Designing an Artificial Neural Network for state evaluation in Arimaa : Using a Convolutional Neural Network.” 2017. Web. 23 May 2019.

Vancouver:

Keisala S. Designing an Artificial Neural Network for state evaluation in Arimaa : Using a Convolutional Neural Network. [Internet] [Thesis]. Linköping University; 2017. [cited 2019 May 23]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143188.

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

Council of Science Editors:

Keisala S. Designing an Artificial Neural Network for state evaluation in Arimaa : Using a Convolutional Neural Network. [Thesis]. Linköping University; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143188

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


University of Arizona

15. Campbell, Tanner. A Deep Learning Approach to Autonomous Relative Terrain Navigation .

Degree: 2017, University of Arizona

 Autonomous relative terrain navigation is a problem at the forefront of many space missions involving close proximity operations to any target body. With no definitive… (more)

Subjects/Keywords: Artificial Intelligence; Autonomous Navigation; Convolutional Neural Network; Deep Neural Network; Relative Terrain Navigation; Spacecraft GNC

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APA (6th Edition):

Campbell, T. (2017). A Deep Learning Approach to Autonomous Relative Terrain Navigation . (Masters Thesis). University of Arizona. Retrieved from http://hdl.handle.net/10150/626706

Chicago Manual of Style (16th Edition):

Campbell, Tanner. “A Deep Learning Approach to Autonomous Relative Terrain Navigation .” 2017. Masters Thesis, University of Arizona. Accessed May 23, 2019. http://hdl.handle.net/10150/626706.

MLA Handbook (7th Edition):

Campbell, Tanner. “A Deep Learning Approach to Autonomous Relative Terrain Navigation .” 2017. Web. 23 May 2019.

Vancouver:

Campbell T. A Deep Learning Approach to Autonomous Relative Terrain Navigation . [Internet] [Masters thesis]. University of Arizona; 2017. [cited 2019 May 23]. Available from: http://hdl.handle.net/10150/626706.

Council of Science Editors:

Campbell T. A Deep Learning Approach to Autonomous Relative Terrain Navigation . [Masters Thesis]. University of Arizona; 2017. Available from: http://hdl.handle.net/10150/626706


University of Miami

16. Xie, Ziqian. Deep Learning Approach for Brain Machine Interface.

Degree: PhD, Biomedical Engineering (Engineering), 2018, University of Miami

 Objective: Brain machine interface (BMI) or Brain Computer Interface (BCI) provides a direct pathway between the brain and an external device to help people suffering… (more)

Subjects/Keywords: brain machine interface; signal processing; recurrent neural network; convolutional neural network; trajectory decoding; connectivity analysis

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

APA (6th Edition):

Xie, Z. (2018). Deep Learning Approach for Brain Machine Interface. (Doctoral Dissertation). University of Miami. Retrieved from https://scholarlyrepository.miami.edu/oa_dissertations/2096

Chicago Manual of Style (16th Edition):

Xie, Ziqian. “Deep Learning Approach for Brain Machine Interface.” 2018. Doctoral Dissertation, University of Miami. Accessed May 23, 2019. https://scholarlyrepository.miami.edu/oa_dissertations/2096.

MLA Handbook (7th Edition):

Xie, Ziqian. “Deep Learning Approach for Brain Machine Interface.” 2018. Web. 23 May 2019.

Vancouver:

Xie Z. Deep Learning Approach for Brain Machine Interface. [Internet] [Doctoral dissertation]. University of Miami; 2018. [cited 2019 May 23]. Available from: https://scholarlyrepository.miami.edu/oa_dissertations/2096.

Council of Science Editors:

Xie Z. Deep Learning Approach for Brain Machine Interface. [Doctoral Dissertation]. University of Miami; 2018. Available from: https://scholarlyrepository.miami.edu/oa_dissertations/2096


NSYSU

17. Wu, Pei-Hsuan. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.

Degree: Master, Computer Science and Engineering, 2018, NSYSU

 Deep Neural Networks (DNN) widely used in computer vision applications have superior performance in image classification and object detection. However, the huge amount of data… (more)

Subjects/Keywords: CNN hardware accelerator; deep neural network (DNN); convolutional neural network (CNN); machine learning

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

APA (6th Edition):

Wu, P. (2018). Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714

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):

Wu, Pei-Hsuan. “Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.” 2018. Thesis, NSYSU. Accessed May 23, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714.

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

MLA Handbook (7th Edition):

Wu, Pei-Hsuan. “Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.” 2018. Web. 23 May 2019.

Vancouver:

Wu P. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. [Internet] [Thesis]. NSYSU; 2018. [cited 2019 May 23]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714.

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

Council of Science Editors:

Wu P. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714

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


University of Bridgeport

18. Hassan, Abdalraouf. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .

Degree: 2018, University of Bridgeport

 The evolution of the social media and the e-commerce sites produces a massive amount of unstructured text data on the internet. Thus, there is a… (more)

Subjects/Keywords: Convolutional neural network; Deep learning; Machine learning; Natural language processing; Recurrent neural network; Sentiment analysis

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

APA (6th Edition):

Hassan, A. (2018). Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks . (Thesis). University of Bridgeport. Retrieved from https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274

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):

Hassan, Abdalraouf. “Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .” 2018. Thesis, University of Bridgeport. Accessed May 23, 2019. https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274.

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

MLA Handbook (7th Edition):

Hassan, Abdalraouf. “Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .” 2018. Web. 23 May 2019.

Vancouver:

Hassan A. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks . [Internet] [Thesis]. University of Bridgeport; 2018. [cited 2019 May 23]. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274.

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

Council of Science Editors:

Hassan A. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks . [Thesis]. University of Bridgeport; 2018. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274

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


Utah State University

19. Tiwari, Astha. A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic.

Degree: MS, Computer Science, 2018, Utah State University

  Colony Collapse Disorder (CCD) has been a major threat to bee colonies around the world which affects vital human food crop pollination. The decline… (more)

Subjects/Keywords: Computer Vision; Deep Learning; Convolutional Neural Network; Bee Traffic; Artificial Neural Network; Computer Sciences

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

APA (6th Edition):

Tiwari, A. (2018). A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7076

Chicago Manual of Style (16th Edition):

Tiwari, Astha. “A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic.” 2018. Masters Thesis, Utah State University. Accessed May 23, 2019. https://digitalcommons.usu.edu/etd/7076.

MLA Handbook (7th Edition):

Tiwari, Astha. “A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic.” 2018. Web. 23 May 2019.

Vancouver:

Tiwari A. A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic. [Internet] [Masters thesis]. Utah State University; 2018. [cited 2019 May 23]. Available from: https://digitalcommons.usu.edu/etd/7076.

Council of Science Editors:

Tiwari A. A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic. [Masters Thesis]. Utah State University; 2018. Available from: https://digitalcommons.usu.edu/etd/7076


Northeastern University

20. Corsaro, Matthew. Robotic grasping in cluttered scenes.

Degree: MS, Computer Science Program, 2017, Northeastern University

 Robotic grasping systems that can clear clutter from a surface have many possible applications. One of these grasping systems could be implemented on a mobile… (more)

Subjects/Keywords: convolutional neural network; deep learning; grasp detection; robotics; UR5

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APA (6th Edition):

Corsaro, M. (2017). Robotic grasping in cluttered scenes. (Masters Thesis). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20248546

Chicago Manual of Style (16th Edition):

Corsaro, Matthew. “Robotic grasping in cluttered scenes.” 2017. Masters Thesis, Northeastern University. Accessed May 23, 2019. http://hdl.handle.net/2047/D20248546.

MLA Handbook (7th Edition):

Corsaro, Matthew. “Robotic grasping in cluttered scenes.” 2017. Web. 23 May 2019.

Vancouver:

Corsaro M. Robotic grasping in cluttered scenes. [Internet] [Masters thesis]. Northeastern University; 2017. [cited 2019 May 23]. Available from: http://hdl.handle.net/2047/D20248546.

Council of Science Editors:

Corsaro M. Robotic grasping in cluttered scenes. [Masters Thesis]. Northeastern University; 2017. Available from: http://hdl.handle.net/2047/D20248546


KTH

21. Schilling, Fabian. The Effect of Batch Normalization on Deep Convolutional Neural Networks.

Degree: CAS, 2016, KTH

Batch normalization is a recently popularized method for accelerating the training of deep feed-forward neural networks. Apart from speed improvements, the technique reportedly enables… (more)

Subjects/Keywords: batch normalization; deep learning; convolutional neural network; Computer Sciences; Datavetenskap (datalogi)

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

APA (6th Edition):

Schilling, F. (2016). The Effect of Batch Normalization on Deep Convolutional Neural Networks. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191222

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):

Schilling, Fabian. “The Effect of Batch Normalization on Deep Convolutional Neural Networks.” 2016. Thesis, KTH. Accessed May 23, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191222.

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

MLA Handbook (7th Edition):

Schilling, Fabian. “The Effect of Batch Normalization on Deep Convolutional Neural Networks.” 2016. Web. 23 May 2019.

Vancouver:

Schilling F. The Effect of Batch Normalization on Deep Convolutional Neural Networks. [Internet] [Thesis]. KTH; 2016. [cited 2019 May 23]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191222.

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

Council of Science Editors:

Schilling F. The Effect of Batch Normalization on Deep Convolutional Neural Networks. [Thesis]. KTH; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191222

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


KTH

22. Fristedt, Hampus. Homography Estimation using Deep Learning for Registering All-22 Football Video Frames.

Degree: Computer Science and Communication (CSC), 2017, KTH

Homography estimation is a fundamental task in many computer vision applications, but many techniques for estimation rely on complicated feature extraction pipelines. We extend… (more)

Subjects/Keywords: Deep learning; Convolutional neural network; homography; Computer Sciences; Datavetenskap (datalogi)

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

APA (6th Edition):

Fristedt, H. (2017). Homography Estimation using Deep Learning for Registering All-22 Football Video Frames. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209583

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):

Fristedt, Hampus. “Homography Estimation using Deep Learning for Registering All-22 Football Video Frames.” 2017. Thesis, KTH. Accessed May 23, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209583.

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

MLA Handbook (7th Edition):

Fristedt, Hampus. “Homography Estimation using Deep Learning for Registering All-22 Football Video Frames.” 2017. Web. 23 May 2019.

Vancouver:

Fristedt H. Homography Estimation using Deep Learning for Registering All-22 Football Video Frames. [Internet] [Thesis]. KTH; 2017. [cited 2019 May 23]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209583.

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

Council of Science Editors:

Fristedt H. Homography Estimation using Deep Learning for Registering All-22 Football Video Frames. [Thesis]. KTH; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209583

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


NSYSU

23. Wang, Li-chieh. System Platform Integration and Kernel Optimizations for Some Embedded Applications Based on Altera OpenCL Framework.

Degree: Master, Computer Science and Engineering, 2017, NSYSU

 In recent years, accelerating compute-intensive applications by utilizing FPGA computing resources based on OpenCL interface has received a lot of attention. This scheme cannot only… (more)

Subjects/Keywords: Altera FPGA; convolutional neural network; HOG; CNN; human detection; OpenCL

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

APA (6th Edition):

Wang, L. (2017). System Platform Integration and Kernel Optimizations for Some Embedded Applications Based on Altera OpenCL Framework. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0016117-135946

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, Li-chieh. “System Platform Integration and Kernel Optimizations for Some Embedded Applications Based on Altera OpenCL Framework.” 2017. Thesis, NSYSU. Accessed May 23, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0016117-135946.

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

MLA Handbook (7th Edition):

Wang, Li-chieh. “System Platform Integration and Kernel Optimizations for Some Embedded Applications Based on Altera OpenCL Framework.” 2017. Web. 23 May 2019.

Vancouver:

Wang L. System Platform Integration and Kernel Optimizations for Some Embedded Applications Based on Altera OpenCL Framework. [Internet] [Thesis]. NSYSU; 2017. [cited 2019 May 23]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0016117-135946.

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

Council of Science Editors:

Wang L. System Platform Integration and Kernel Optimizations for Some Embedded Applications Based on Altera OpenCL Framework. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0016117-135946

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


University of Dayton

24. Martell, Patrick Keith. Hierarchical Auto-Associative Polynomial Convolutional Neural Networks.

Degree: MS(M.S.), Electrical Engineering, 2017, University of Dayton

Convolutional neural networks (CNNs) lack ample methods to improve performance without either adding more input data, modifying existing data, or changing network design. This work… (more)

Subjects/Keywords: Electrical Engineering; Convolutional Neural Network; Polynomial; CNN; Classification; MNIST

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APA (6th Edition):

Martell, P. K. (2017). Hierarchical Auto-Associative Polynomial Convolutional Neural Networks. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038

Chicago Manual of Style (16th Edition):

Martell, Patrick Keith. “Hierarchical Auto-Associative Polynomial Convolutional Neural Networks.” 2017. Masters Thesis, University of Dayton. Accessed May 23, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038.

MLA Handbook (7th Edition):

Martell, Patrick Keith. “Hierarchical Auto-Associative Polynomial Convolutional Neural Networks.” 2017. Web. 23 May 2019.

Vancouver:

Martell PK. Hierarchical Auto-Associative Polynomial Convolutional Neural Networks. [Internet] [Masters thesis]. University of Dayton; 2017. [cited 2019 May 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038.

Council of Science Editors:

Martell PK. Hierarchical Auto-Associative Polynomial Convolutional Neural Networks. [Masters Thesis]. University of Dayton; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038


University of Akron

25. Bhattarai, Smrity. Digital Architecture for real-time face detection for deep video packet inspection systems.

Degree: MS, Electrical Engineering, 2017, University of Akron

 Face detection and optional recognition is a highly researched area in digital imageprocessing. Face detection allows gathering of statistical data from video sequences,with applications in… (more)

Subjects/Keywords: Electrical Engineering; Face detection, Convolutional Neural Network, Image processing

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

APA (6th Edition):

Bhattarai, S. (2017). Digital Architecture for real-time face detection for deep video packet inspection systems. (Masters Thesis). University of Akron. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=akron1492787219112947

Chicago Manual of Style (16th Edition):

Bhattarai, Smrity. “Digital Architecture for real-time face detection for deep video packet inspection systems.” 2017. Masters Thesis, University of Akron. Accessed May 23, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=akron1492787219112947.

MLA Handbook (7th Edition):

Bhattarai, Smrity. “Digital Architecture for real-time face detection for deep video packet inspection systems.” 2017. Web. 23 May 2019.

Vancouver:

Bhattarai S. Digital Architecture for real-time face detection for deep video packet inspection systems. [Internet] [Masters thesis]. University of Akron; 2017. [cited 2019 May 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=akron1492787219112947.

Council of Science Editors:

Bhattarai S. Digital Architecture for real-time face detection for deep video packet inspection systems. [Masters Thesis]. University of Akron; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=akron1492787219112947


Carnegie Mellon University

26. Yu, Zhiding. Learning Structured and Deep Representations for Traffc Scene Understanding.

Degree: 2017, Carnegie Mellon University

 Recent advances in representation learning have led to an increasing variety of vision-based approaches in traffic scene understanding. This includes general vision problems such as… (more)

Subjects/Keywords: computer vision; convolutional neural network; deep learning; scene understanding; structured prediction

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

APA (6th Edition):

Yu, Z. (2017). Learning Structured and Deep Representations for Traffc Scene Understanding. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/1109

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):

Yu, Zhiding. “Learning Structured and Deep Representations for Traffc Scene Understanding.” 2017. Thesis, Carnegie Mellon University. Accessed May 23, 2019. http://repository.cmu.edu/dissertations/1109.

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

MLA Handbook (7th Edition):

Yu, Zhiding. “Learning Structured and Deep Representations for Traffc Scene Understanding.” 2017. Web. 23 May 2019.

Vancouver:

Yu Z. Learning Structured and Deep Representations for Traffc Scene Understanding. [Internet] [Thesis]. Carnegie Mellon University; 2017. [cited 2019 May 23]. Available from: http://repository.cmu.edu/dissertations/1109.

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

Council of Science Editors:

Yu Z. Learning Structured and Deep Representations for Traffc Scene Understanding. [Thesis]. Carnegie Mellon University; 2017. Available from: http://repository.cmu.edu/dissertations/1109

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


University of California – San Diego

27. Du, Lawrence. Exploring cis-regulation in C. elegans gut development with single molecule resolution and deep learning techniques.

Degree: Biology, 2017, University of California – San Diego

 A long-term goal of biology has been to understand how DNA sequences specify complex multicellular living organisms. Understanding this mapping of sequence to form requires… (more)

Subjects/Keywords: Biology; Bioinformatics; c. elegans; convolutional; endoderm; neural network; promoter; transcription

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APA (6th Edition):

Du, L. (2017). Exploring cis-regulation in C. elegans gut development with single molecule resolution and deep learning techniques. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/5n29k0vj

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):

Du, Lawrence. “Exploring cis-regulation in C. elegans gut development with single molecule resolution and deep learning techniques.” 2017. Thesis, University of California – San Diego. Accessed May 23, 2019. http://www.escholarship.org/uc/item/5n29k0vj.

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

MLA Handbook (7th Edition):

Du, Lawrence. “Exploring cis-regulation in C. elegans gut development with single molecule resolution and deep learning techniques.” 2017. Web. 23 May 2019.

Vancouver:

Du L. Exploring cis-regulation in C. elegans gut development with single molecule resolution and deep learning techniques. [Internet] [Thesis]. University of California – San Diego; 2017. [cited 2019 May 23]. Available from: http://www.escholarship.org/uc/item/5n29k0vj.

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

Council of Science Editors:

Du L. Exploring cis-regulation in C. elegans gut development with single molecule resolution and deep learning techniques. [Thesis]. University of California – San Diego; 2017. Available from: http://www.escholarship.org/uc/item/5n29k0vj

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


University of Sydney

28. Yeung, Henry Wing Fung. Object Tracking with Deep Learning and Swarm Intelligence .

Degree: 2017, University of Sydney

 Swarm Intelligence has been applied to object tracking in the recent decade. Despite the algorithm has consistently improved overtime, Swarm Intelligence based object trackers still… (more)

Subjects/Keywords: Object Tracking; Particle Swarm Optimization; Deep Learning; Convolutional Neural Network

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

APA (6th Edition):

Yeung, H. W. F. (2017). Object Tracking with Deep Learning and Swarm Intelligence . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/16834

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):

Yeung, Henry Wing Fung. “Object Tracking with Deep Learning and Swarm Intelligence .” 2017. Thesis, University of Sydney. Accessed May 23, 2019. http://hdl.handle.net/2123/16834.

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

MLA Handbook (7th Edition):

Yeung, Henry Wing Fung. “Object Tracking with Deep Learning and Swarm Intelligence .” 2017. Web. 23 May 2019.

Vancouver:

Yeung HWF. Object Tracking with Deep Learning and Swarm Intelligence . [Internet] [Thesis]. University of Sydney; 2017. [cited 2019 May 23]. Available from: http://hdl.handle.net/2123/16834.

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

Council of Science Editors:

Yeung HWF. Object Tracking with Deep Learning and Swarm Intelligence . [Thesis]. University of Sydney; 2017. Available from: http://hdl.handle.net/2123/16834

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


King Abdullah University of Science and Technology

29. Wang, Su. Exploring Ocean Animal Trajectory Pattern via Deep Learning.

Degree: 2016, King Abdullah University of Science and Technology

 We trained a combined deep convolutional neural network to predict seals’ age (3 categories) and gender (2 categories). The entire dataset contains 110 seals with… (more)

Subjects/Keywords: deep learning; animal trajectory; convolutional neural network; feature representation

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

APA (6th Edition):

Wang, S. (2016). Exploring Ocean Animal Trajectory Pattern via Deep Learning. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/610580

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, Su. “Exploring Ocean Animal Trajectory Pattern via Deep Learning.” 2016. Thesis, King Abdullah University of Science and Technology. Accessed May 23, 2019. http://hdl.handle.net/10754/610580.

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

MLA Handbook (7th Edition):

Wang, Su. “Exploring Ocean Animal Trajectory Pattern via Deep Learning.” 2016. Web. 23 May 2019.

Vancouver:

Wang S. Exploring Ocean Animal Trajectory Pattern via Deep Learning. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2016. [cited 2019 May 23]. Available from: http://hdl.handle.net/10754/610580.

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

Council of Science Editors:

Wang S. Exploring Ocean Animal Trajectory Pattern via Deep Learning. [Thesis]. King Abdullah University of Science and Technology; 2016. Available from: http://hdl.handle.net/10754/610580

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

30. Hodges, Jonathan Lee. Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks.

Degree: PhD, Mechanical Engineering, 2018, Virginia Tech

 Fire dynamics is a complex process involving multi-mode heat transfer, reacting fluid flow, and the reaction of combustible materials. High-fidelity predictions of fire behavior using… (more)

Subjects/Keywords: Wildland; Structure; Fire; Artificial; Neural; Network; Convolutional; CNN

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

APA (6th Edition):

Hodges, J. L. (2018). Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/86364

Chicago Manual of Style (16th Edition):

Hodges, Jonathan Lee. “Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks.” 2018. Doctoral Dissertation, Virginia Tech. Accessed May 23, 2019. http://hdl.handle.net/10919/86364.

MLA Handbook (7th Edition):

Hodges, Jonathan Lee. “Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks.” 2018. Web. 23 May 2019.

Vancouver:

Hodges JL. Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2019 May 23]. Available from: http://hdl.handle.net/10919/86364.

Council of Science Editors:

Hodges JL. Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/86364

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