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You searched for subject:(Recurrent Neural Network). Showing records 1 – 30 of 99 total matches.

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Penn State University

1. Lin, Tao. A DATA TRIAGE RETRIEVAL SYSTEM FOR CYBER SECURITY OPERATIONS CENTER.

Degree: 2018, Penn State University

 Triage analysis is a fundamental stage in cyber operations in Security Operations Centers (SOCs). The massive data sources generate great demands on cyber security analysts'… (more)

Subjects/Keywords: Recurrent Neural Network; Machine Learning; Retrieval; Security

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

APA (6th Edition):

Lin, T. (2018). A DATA TRIAGE RETRIEVAL SYSTEM FOR CYBER SECURITY OPERATIONS CENTER. (Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/14787txl78

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

Chicago Manual of Style (16th Edition):

Lin, Tao. “A DATA TRIAGE RETRIEVAL SYSTEM FOR CYBER SECURITY OPERATIONS CENTER.” 2018. Thesis, Penn State University. Accessed November 18, 2019. https://etda.libraries.psu.edu/catalog/14787txl78.

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

MLA Handbook (7th Edition):

Lin, Tao. “A DATA TRIAGE RETRIEVAL SYSTEM FOR CYBER SECURITY OPERATIONS CENTER.” 2018. Web. 18 Nov 2019.

Vancouver:

Lin T. A DATA TRIAGE RETRIEVAL SYSTEM FOR CYBER SECURITY OPERATIONS CENTER. [Internet] [Thesis]. Penn State University; 2018. [cited 2019 Nov 18]. Available from: https://etda.libraries.psu.edu/catalog/14787txl78.

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

Council of Science Editors:

Lin T. A DATA TRIAGE RETRIEVAL SYSTEM FOR CYBER SECURITY OPERATIONS CENTER. [Thesis]. Penn State University; 2018. Available from: https://etda.libraries.psu.edu/catalog/14787txl78

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


Mid Sweden University

2. 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 November 18, 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. 18 Nov 2019.

Vancouver:

Wang X. Chinese Text Classification Based On Deep Learning. [Internet] [Thesis]. Mid Sweden University; 2018. [cited 2019 Nov 18]. 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


Mid Sweden University

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

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 November 18, 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. 18 Nov 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 Nov 18]. 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


Rochester Institute of Technology

4. 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 November 18, 2019. https://scholarworks.rit.edu/theses/9516.

MLA Handbook (7th Edition):

Nguyen, Thang Huy. “Automatic Video Captioning using Deep Neural Network.” 2017. Web. 18 Nov 2019.

Vancouver:

Nguyen TH. Automatic Video Captioning using Deep Neural Network. [Internet] [Masters thesis]. Rochester Institute of Technology; 2017. [cited 2019 Nov 18]. 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

5. Bidgoli, Rohollah Soltani. Higher Order Recurrent Neural Network for Language Modeling.

Degree: MSc -MS, Computer Science, 2016, York University

 In this thesis, we study novel neural network structures to better model long term dependency in sequential data. We propose to use more memory units… (more)

Subjects/Keywords: Computer science; Machine Learning; Deep Learning; Neural Network; Recurrent Neural Network; Language Modeling; Higher Order Recurrent Neural Network

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

APA (6th Edition):

Bidgoli, R. S. (2016). Higher Order Recurrent Neural Network for Language Modeling. (Masters Thesis). York University. Retrieved from http://hdl.handle.net/10315/32337

Chicago Manual of Style (16th Edition):

Bidgoli, Rohollah Soltani. “Higher Order Recurrent Neural Network for Language Modeling.” 2016. Masters Thesis, York University. Accessed November 18, 2019. http://hdl.handle.net/10315/32337.

MLA Handbook (7th Edition):

Bidgoli, Rohollah Soltani. “Higher Order Recurrent Neural Network for Language Modeling.” 2016. Web. 18 Nov 2019.

Vancouver:

Bidgoli RS. Higher Order Recurrent Neural Network for Language Modeling. [Internet] [Masters thesis]. York University; 2016. [cited 2019 Nov 18]. Available from: http://hdl.handle.net/10315/32337.

Council of Science Editors:

Bidgoli RS. Higher Order Recurrent Neural Network for Language Modeling. [Masters Thesis]. York University; 2016. Available from: http://hdl.handle.net/10315/32337


Tampere University

6. Zhou, Yi. Sentiment classification with deep neural networks .

Degree: 2019, Tampere University

 Sentiment classification is an important task in Natural Language Processing (NLP) area. Deep neural networks become the mainstream method to perform the text sentiment classification… (more)

Subjects/Keywords: deep neural networks; convolutional neural network; recurrent neural network; sentiment classification; hotel reviews; TripAdvisor

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APA (6th Edition):

Zhou, Y. (2019). Sentiment classification with deep neural networks . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi//handle/10024/116148

Chicago Manual of Style (16th Edition):

Zhou, Yi. “Sentiment classification with deep neural networks .” 2019. Masters Thesis, Tampere University. Accessed November 18, 2019. https://trepo.tuni.fi//handle/10024/116148.

MLA Handbook (7th Edition):

Zhou, Yi. “Sentiment classification with deep neural networks .” 2019. Web. 18 Nov 2019.

Vancouver:

Zhou Y. Sentiment classification with deep neural networks . [Internet] [Masters thesis]. Tampere University; 2019. [cited 2019 Nov 18]. Available from: https://trepo.tuni.fi//handle/10024/116148.

Council of Science Editors:

Zhou Y. Sentiment classification with deep neural networks . [Masters Thesis]. Tampere University; 2019. Available from: https://trepo.tuni.fi//handle/10024/116148


University of Illinois – Urbana-Champaign

7. 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 (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 November 18, 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. 18 Nov 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 Nov 18]. 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


Carnegie Mellon University

8. Le, Ngan Thi Hoang. Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling.

Degree: 2018, Carnegie Mellon University

 Semantic labeling is becoming more and more popular among researchers in computer vision and machine learning. Many applications, such as autonomous driving, tracking, indoor navigation,… (more)

Subjects/Keywords: Gated Recurrent Unit; Level Set; Recurrent Neural Networks; Residual Network; Scene Labeling; Semantic Instance Segmentation

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APA (6th Edition):

Le, N. T. H. (2018). Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/1166

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):

Le, Ngan Thi Hoang. “Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling.” 2018. Thesis, Carnegie Mellon University. Accessed November 18, 2019. http://repository.cmu.edu/dissertations/1166.

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

MLA Handbook (7th Edition):

Le, Ngan Thi Hoang. “Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling.” 2018. Web. 18 Nov 2019.

Vancouver:

Le NTH. Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling. [Internet] [Thesis]. Carnegie Mellon University; 2018. [cited 2019 Nov 18]. Available from: http://repository.cmu.edu/dissertations/1166.

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

Council of Science Editors:

Le NTH. Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling. [Thesis]. Carnegie Mellon University; 2018. Available from: http://repository.cmu.edu/dissertations/1166

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


University of Miami

9. 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 November 18, 2019. https://scholarlyrepository.miami.edu/oa_dissertations/2096.

MLA Handbook (7th Edition):

Xie, Ziqian. “Deep Learning Approach for Brain Machine Interface.” 2018. Web. 18 Nov 2019.

Vancouver:

Xie Z. Deep Learning Approach for Brain Machine Interface. [Internet] [Doctoral dissertation]. University of Miami; 2018. [cited 2019 Nov 18]. 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


University of California – Berkeley

10. Thanapirom, Chayut. Neural Representation Learning with Denoising Autoencoder Framework.

Degree: Physics, 2016, University of California – Berkeley

 Understanding of how the brain works and how it can solve difficult problems like image recognition is very important, especially for the progress in developing… (more)

Subjects/Keywords: Biophysics; Neurosciences; Attractor Network; Denoising Autoencoder; Grid Cells; Neural Representation; Recurrent Neural Network; Sparse Coding

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APA (6th Edition):

Thanapirom, C. (2016). Neural Representation Learning with Denoising Autoencoder Framework. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/0hm6p6s5

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):

Thanapirom, Chayut. “Neural Representation Learning with Denoising Autoencoder Framework.” 2016. Thesis, University of California – Berkeley. Accessed November 18, 2019. http://www.escholarship.org/uc/item/0hm6p6s5.

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

MLA Handbook (7th Edition):

Thanapirom, Chayut. “Neural Representation Learning with Denoising Autoencoder Framework.” 2016. Web. 18 Nov 2019.

Vancouver:

Thanapirom C. Neural Representation Learning with Denoising Autoencoder Framework. [Internet] [Thesis]. University of California – Berkeley; 2016. [cited 2019 Nov 18]. Available from: http://www.escholarship.org/uc/item/0hm6p6s5.

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

Council of Science Editors:

Thanapirom C. Neural Representation Learning with Denoising Autoencoder Framework. [Thesis]. University of California – Berkeley; 2016. Available from: http://www.escholarship.org/uc/item/0hm6p6s5

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


University of Bridgeport

11. 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 November 18, 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. 18 Nov 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 Nov 18]. 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


Clemson University

12. Wang, Xiaoyu. ARCHITECTURE OPTIMIZATION, TRAINING CONVERGENCE AND NETWORK ESTIMATION ROBUSTNESS OF A FULLY CONNECTED RECURRENT NEURAL NETWORK.

Degree: PhD, Mechanical Engineering, 2010, Clemson University

Recurrent neural networks (RNN) have been rapidly developed in recent years. Applications of RNN can be found in system identification, optimization, image processing, pattern reorganization,… (more)

Subjects/Keywords: Network Optimization; Network Robustness; Recurrent Neural Network; Training Algorithm; Training Convergence; Artificial Intelligence and Robotics

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APA (6th Edition):

Wang, X. (2010). ARCHITECTURE OPTIMIZATION, TRAINING CONVERGENCE AND NETWORK ESTIMATION ROBUSTNESS OF A FULLY CONNECTED RECURRENT NEURAL NETWORK. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/536

Chicago Manual of Style (16th Edition):

Wang, Xiaoyu. “ARCHITECTURE OPTIMIZATION, TRAINING CONVERGENCE AND NETWORK ESTIMATION ROBUSTNESS OF A FULLY CONNECTED RECURRENT NEURAL NETWORK.” 2010. Doctoral Dissertation, Clemson University. Accessed November 18, 2019. https://tigerprints.clemson.edu/all_dissertations/536.

MLA Handbook (7th Edition):

Wang, Xiaoyu. “ARCHITECTURE OPTIMIZATION, TRAINING CONVERGENCE AND NETWORK ESTIMATION ROBUSTNESS OF A FULLY CONNECTED RECURRENT NEURAL NETWORK.” 2010. Web. 18 Nov 2019.

Vancouver:

Wang X. ARCHITECTURE OPTIMIZATION, TRAINING CONVERGENCE AND NETWORK ESTIMATION ROBUSTNESS OF A FULLY CONNECTED RECURRENT NEURAL NETWORK. [Internet] [Doctoral dissertation]. Clemson University; 2010. [cited 2019 Nov 18]. Available from: https://tigerprints.clemson.edu/all_dissertations/536.

Council of Science Editors:

Wang X. ARCHITECTURE OPTIMIZATION, TRAINING CONVERGENCE AND NETWORK ESTIMATION ROBUSTNESS OF A FULLY CONNECTED RECURRENT NEURAL NETWORK. [Doctoral Dissertation]. Clemson University; 2010. Available from: https://tigerprints.clemson.edu/all_dissertations/536

13. Herzfeld, David James. Modeling and Computational Framework for the Specification and Simulation of Large-scale Spiking Neural Networks.

Degree: 2011, Marquette University

 Recurrently connected neural networks, in which synaptic connections between neurons can form directed cycles, have been used extensively in the literature to describe various neurophysiological… (more)

Subjects/Keywords: hemodynamic; neural network; recurrent; spiking; Biomedical Engineering and Bioengineering

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APA (6th Edition):

Herzfeld, D. J. (2011). Modeling and Computational Framework for the Specification and Simulation of Large-scale Spiking Neural Networks. (Thesis). Marquette University. Retrieved from https://epublications.marquette.edu/theses_open/102

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):

Herzfeld, David James. “Modeling and Computational Framework for the Specification and Simulation of Large-scale Spiking Neural Networks.” 2011. Thesis, Marquette University. Accessed November 18, 2019. https://epublications.marquette.edu/theses_open/102.

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

MLA Handbook (7th Edition):

Herzfeld, David James. “Modeling and Computational Framework for the Specification and Simulation of Large-scale Spiking Neural Networks.” 2011. Web. 18 Nov 2019.

Vancouver:

Herzfeld DJ. Modeling and Computational Framework for the Specification and Simulation of Large-scale Spiking Neural Networks. [Internet] [Thesis]. Marquette University; 2011. [cited 2019 Nov 18]. Available from: https://epublications.marquette.edu/theses_open/102.

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

Council of Science Editors:

Herzfeld DJ. Modeling and Computational Framework for the Specification and Simulation of Large-scale Spiking Neural Networks. [Thesis]. Marquette University; 2011. Available from: https://epublications.marquette.edu/theses_open/102

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


UCLA

14. Li, Siyuan. Application of Recurrent Neural Networks In Toxic Comment Classification.

Degree: Statistics, 2018, UCLA

 Moderators of online discussion forums often struggle with controlling extremist comments on their platforms. To help provide an efficient and accurate tool to detect online… (more)

Subjects/Keywords: Statistics; classification; natural language processing; recurrent neural network; word2vec

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APA (6th Edition):

Li, S. (2018). Application of Recurrent Neural Networks In Toxic Comment Classification. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/5f87h061

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):

Li, Siyuan. “Application of Recurrent Neural Networks In Toxic Comment Classification.” 2018. Thesis, UCLA. Accessed November 18, 2019. http://www.escholarship.org/uc/item/5f87h061.

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

MLA Handbook (7th Edition):

Li, Siyuan. “Application of Recurrent Neural Networks In Toxic Comment Classification.” 2018. Web. 18 Nov 2019.

Vancouver:

Li S. Application of Recurrent Neural Networks In Toxic Comment Classification. [Internet] [Thesis]. UCLA; 2018. [cited 2019 Nov 18]. Available from: http://www.escholarship.org/uc/item/5f87h061.

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

Council of Science Editors:

Li S. Application of Recurrent Neural Networks In Toxic Comment Classification. [Thesis]. UCLA; 2018. Available from: http://www.escholarship.org/uc/item/5f87h061

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


University of Ottawa

15. Taylor, Adrian. Anomaly-Based Detection of Malicious Activity in In-Vehicle Networks .

Degree: 2017, University of Ottawa

 Modern automobiles have been proven vulnerable to hacking by security researchers. By exploiting vulnerabilities in the car's external interfaces, attackers can access a car's controller… (more)

Subjects/Keywords: anomaly detection; cyber security; intrusion detection; recurrent neural network

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APA (6th Edition):

Taylor, A. (2017). Anomaly-Based Detection of Malicious Activity in In-Vehicle Networks . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/36120

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):

Taylor, Adrian. “Anomaly-Based Detection of Malicious Activity in In-Vehicle Networks .” 2017. Thesis, University of Ottawa. Accessed November 18, 2019. http://hdl.handle.net/10393/36120.

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

MLA Handbook (7th Edition):

Taylor, Adrian. “Anomaly-Based Detection of Malicious Activity in In-Vehicle Networks .” 2017. Web. 18 Nov 2019.

Vancouver:

Taylor A. Anomaly-Based Detection of Malicious Activity in In-Vehicle Networks . [Internet] [Thesis]. University of Ottawa; 2017. [cited 2019 Nov 18]. Available from: http://hdl.handle.net/10393/36120.

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

Council of Science Editors:

Taylor A. Anomaly-Based Detection of Malicious Activity in In-Vehicle Networks . [Thesis]. University of Ottawa; 2017. Available from: http://hdl.handle.net/10393/36120

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


University of Waterloo

16. Ruvinov, Igor. Recurrent Neural Network Dual Resistance Control of Multiple Memory Shape Memory Alloys.

Degree: 2018, University of Waterloo

 Shape memory alloys (SMAs) are materials with extraordinary thermomechanical properties which have caused numerous engineering advances. NiTi SMAs in particular have been studied for decades… (more)

Subjects/Keywords: Recurrent neural network; Shape memory alloys; Artificial intelligence

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

APA (6th Edition):

Ruvinov, I. (2018). Recurrent Neural Network Dual Resistance Control of Multiple Memory Shape Memory Alloys. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/13647

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):

Ruvinov, Igor. “Recurrent Neural Network Dual Resistance Control of Multiple Memory Shape Memory Alloys.” 2018. Thesis, University of Waterloo. Accessed November 18, 2019. http://hdl.handle.net/10012/13647.

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

MLA Handbook (7th Edition):

Ruvinov, Igor. “Recurrent Neural Network Dual Resistance Control of Multiple Memory Shape Memory Alloys.” 2018. Web. 18 Nov 2019.

Vancouver:

Ruvinov I. Recurrent Neural Network Dual Resistance Control of Multiple Memory Shape Memory Alloys. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2019 Nov 18]. Available from: http://hdl.handle.net/10012/13647.

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

Council of Science Editors:

Ruvinov I. Recurrent Neural Network Dual Resistance Control of Multiple Memory Shape Memory Alloys. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/13647

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


Victoria University of Wellington

17. Chandra, Rohitash. Problem Decomposition and Adaptation in Cooperative Neuro-Evolution.

Degree: 2012, Victoria University of Wellington

 One way to train neural networks is to use evolutionary algorithms such as cooperative coevolution - a method that decomposes the network's learnable parameters into… (more)

Subjects/Keywords: Neural networks; Cooperative coevolution; Recurrent network; Co-operative co-evolution

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APA (6th Edition):

Chandra, R. (2012). Problem Decomposition and Adaptation in Cooperative Neuro-Evolution. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/2110

Chicago Manual of Style (16th Edition):

Chandra, Rohitash. “Problem Decomposition and Adaptation in Cooperative Neuro-Evolution.” 2012. Doctoral Dissertation, Victoria University of Wellington. Accessed November 18, 2019. http://hdl.handle.net/10063/2110.

MLA Handbook (7th Edition):

Chandra, Rohitash. “Problem Decomposition and Adaptation in Cooperative Neuro-Evolution.” 2012. Web. 18 Nov 2019.

Vancouver:

Chandra R. Problem Decomposition and Adaptation in Cooperative Neuro-Evolution. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2012. [cited 2019 Nov 18]. Available from: http://hdl.handle.net/10063/2110.

Council of Science Editors:

Chandra R. Problem Decomposition and Adaptation in Cooperative Neuro-Evolution. [Doctoral Dissertation]. Victoria University of Wellington; 2012. Available from: http://hdl.handle.net/10063/2110


University of New Mexico

18. Goudarzi, Alireza. Theory and Practice of Computing with Excitable Dynamics.

Degree: Department of Computer Science, 2016, University of New Mexico

  Reservoir computing (RC) is a promising paradigm for time series processing. In this paradigm, the desired output is computed by combining measurements of an… (more)

Subjects/Keywords: reservoir computing; recurrent neural network; excitable dynamics; dynamical systems; Computer Sciences

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

APA (6th Edition):

Goudarzi, A. (2016). Theory and Practice of Computing with Excitable Dynamics. (Doctoral Dissertation). University of New Mexico. Retrieved from https://digitalrepository.unm.edu/cs_etds/81

Chicago Manual of Style (16th Edition):

Goudarzi, Alireza. “Theory and Practice of Computing with Excitable Dynamics.” 2016. Doctoral Dissertation, University of New Mexico. Accessed November 18, 2019. https://digitalrepository.unm.edu/cs_etds/81.

MLA Handbook (7th Edition):

Goudarzi, Alireza. “Theory and Practice of Computing with Excitable Dynamics.” 2016. Web. 18 Nov 2019.

Vancouver:

Goudarzi A. Theory and Practice of Computing with Excitable Dynamics. [Internet] [Doctoral dissertation]. University of New Mexico; 2016. [cited 2019 Nov 18]. Available from: https://digitalrepository.unm.edu/cs_etds/81.

Council of Science Editors:

Goudarzi A. Theory and Practice of Computing with Excitable Dynamics. [Doctoral Dissertation]. University of New Mexico; 2016. Available from: https://digitalrepository.unm.edu/cs_etds/81

19. Oskarsson, Gustav. Aktieprediktion med neurala nätverk : En jämförelse av statistiska modeller, neurala nätverk och kombinerade neurala nätverk.

Degree: 2019, , Department of Industrial Economics

This study is about prediction of the stockmarket through a comparison of neural networks and statistical models. The study aims to improve the accuracy… (more)

Subjects/Keywords: neural network; stock market; recurrent neural network; neuralt nätverk; aktiemarknad; recurrent neural network; Other Engineering and Technologies not elsewhere specified; Övrig annan teknik

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APA (6th Edition):

Oskarsson, G. (2019). Aktieprediktion med neurala nätverk : En jämförelse av statistiska modeller, neurala nätverk och kombinerade neurala nätverk. (Thesis). , Department of Industrial Economics. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18214

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):

Oskarsson, Gustav. “Aktieprediktion med neurala nätverk : En jämförelse av statistiska modeller, neurala nätverk och kombinerade neurala nätverk.” 2019. Thesis, , Department of Industrial Economics. Accessed November 18, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18214.

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

MLA Handbook (7th Edition):

Oskarsson, Gustav. “Aktieprediktion med neurala nätverk : En jämförelse av statistiska modeller, neurala nätverk och kombinerade neurala nätverk.” 2019. Web. 18 Nov 2019.

Vancouver:

Oskarsson G. Aktieprediktion med neurala nätverk : En jämförelse av statistiska modeller, neurala nätverk och kombinerade neurala nätverk. [Internet] [Thesis]. , Department of Industrial Economics; 2019. [cited 2019 Nov 18]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18214.

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

Council of Science Editors:

Oskarsson G. Aktieprediktion med neurala nätverk : En jämförelse av statistiska modeller, neurala nätverk och kombinerade neurala nätverk. [Thesis]. , Department of Industrial Economics; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18214

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


Oklahoma State University

20. Phan, Manh Cong. Recurrent Neural Networks: Error Surface Analysis and Improved Training.

Degree: Electrical Engineering, 2014, Oklahoma State University

Recurrent neural networks (RNNs) have powerful computational abilities and could be used in a variety of applications; however, training these networks is still a difficult… (more)

Subjects/Keywords: error surface; neural control; recurrent neural network; spurious valley; system identification; training

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

APA (6th Edition):

Phan, M. C. (2014). Recurrent Neural Networks: Error Surface Analysis and Improved Training. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/15063

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):

Phan, Manh Cong. “Recurrent Neural Networks: Error Surface Analysis and Improved Training.” 2014. Thesis, Oklahoma State University. Accessed November 18, 2019. http://hdl.handle.net/11244/15063.

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

MLA Handbook (7th Edition):

Phan, Manh Cong. “Recurrent Neural Networks: Error Surface Analysis and Improved Training.” 2014. Web. 18 Nov 2019.

Vancouver:

Phan MC. Recurrent Neural Networks: Error Surface Analysis and Improved Training. [Internet] [Thesis]. Oklahoma State University; 2014. [cited 2019 Nov 18]. Available from: http://hdl.handle.net/11244/15063.

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

Council of Science Editors:

Phan MC. Recurrent Neural Networks: Error Surface Analysis and Improved Training. [Thesis]. Oklahoma State University; 2014. Available from: http://hdl.handle.net/11244/15063

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


Louisiana State University

21. Firth, Robert James. A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting.

Degree: PhD, Computer Sciences, 2016, Louisiana State University

 Numerical weather prediction is a computationally expensive task that requires not only the numerical solution to a complex set of non-linear partial differential equations, but… (more)

Subjects/Keywords: time series; spatial; temporal; time step network; convolutional; recurrent; neural network; weather forecasting; ocean forecasting

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APA (6th Edition):

Firth, R. J. (2016). A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting. (Doctoral Dissertation). Louisiana State University. Retrieved from etd-04112016-151259 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2099

Chicago Manual of Style (16th Edition):

Firth, Robert James. “A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting.” 2016. Doctoral Dissertation, Louisiana State University. Accessed November 18, 2019. etd-04112016-151259 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2099.

MLA Handbook (7th Edition):

Firth, Robert James. “A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting.” 2016. Web. 18 Nov 2019.

Vancouver:

Firth RJ. A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting. [Internet] [Doctoral dissertation]. Louisiana State University; 2016. [cited 2019 Nov 18]. Available from: etd-04112016-151259 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2099.

Council of Science Editors:

Firth RJ. A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting. [Doctoral Dissertation]. Louisiana State University; 2016. Available from: etd-04112016-151259 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2099


Georgia Tech

22. Chen, Shiyang. Spatiotemporal modeling of brain dynamics using machine learning approaches.

Degree: PhD, Biomedical Engineering (Joint GT/Emory Department), 2017, Georgia Tech

 Resting state fMRI (rfMRI) has been widely used to study functional connectivity of human brains. Although most of the analysis methods to date have assumed… (more)

Subjects/Keywords: Functional magnetic resonance imaging; Resting-state; Dynamic model; Individual identification; Gated recurrent unit; Recurrent neural network

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APA (6th Edition):

Chen, S. (2017). Spatiotemporal modeling of brain dynamics using machine learning approaches. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60677

Chicago Manual of Style (16th Edition):

Chen, Shiyang. “Spatiotemporal modeling of brain dynamics using machine learning approaches.” 2017. Doctoral Dissertation, Georgia Tech. Accessed November 18, 2019. http://hdl.handle.net/1853/60677.

MLA Handbook (7th Edition):

Chen, Shiyang. “Spatiotemporal modeling of brain dynamics using machine learning approaches.” 2017. Web. 18 Nov 2019.

Vancouver:

Chen S. Spatiotemporal modeling of brain dynamics using machine learning approaches. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Nov 18]. Available from: http://hdl.handle.net/1853/60677.

Council of Science Editors:

Chen S. Spatiotemporal modeling of brain dynamics using machine learning approaches. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/60677


Virginia Tech

23. Wang, Wei. Event Detection and Extraction from News Articles.

Degree: PhD, Computer Science, 2018, Virginia Tech

 Event extraction is a type of information extraction(IE) that works on extracting the specific knowledge of certain incidents from texts. Nowadays the amount of available… (more)

Subjects/Keywords: Event Detection; Event Encoding; Deep Learning; Convolutional Neural Network; Recurrent Neural Network; Multi Instance Learning; Multi Task Learning

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

APA (6th Edition):

Wang, W. (2018). Event Detection and Extraction from News Articles. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/82238

Chicago Manual of Style (16th Edition):

Wang, Wei. “Event Detection and Extraction from News Articles.” 2018. Doctoral Dissertation, Virginia Tech. Accessed November 18, 2019. http://hdl.handle.net/10919/82238.

MLA Handbook (7th Edition):

Wang, Wei. “Event Detection and Extraction from News Articles.” 2018. Web. 18 Nov 2019.

Vancouver:

Wang W. Event Detection and Extraction from News Articles. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2019 Nov 18]. Available from: http://hdl.handle.net/10919/82238.

Council of Science Editors:

Wang W. Event Detection and Extraction from News Articles. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/82238

24. Hijazi, Issa. Animal ID Tag Recognition with Convolutional and Recurrent Neural Network : Identifying digits from a number sequence with RCNN.

Degree: Informatics, 2019, University of Skövde

  Major advances in machine learning have made image recognition applications, with Artificial Neural Network, blossom over the recent years. The aim of this thesis… (more)

Subjects/Keywords: Machine Learning; Convolutional Neural Network; Recurrent Neural Network; Transfer learning; Microsoft Azure; Computer Sciences; Datavetenskap (datalogi)

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

APA (6th Edition):

Hijazi, I. (2019). Animal ID Tag Recognition with Convolutional and Recurrent Neural Network : Identifying digits from a number sequence with RCNN. (Thesis). University of Skövde. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17031

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):

Hijazi, Issa. “Animal ID Tag Recognition with Convolutional and Recurrent Neural Network : Identifying digits from a number sequence with RCNN.” 2019. Thesis, University of Skövde. Accessed November 18, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17031.

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

MLA Handbook (7th Edition):

Hijazi, Issa. “Animal ID Tag Recognition with Convolutional and Recurrent Neural Network : Identifying digits from a number sequence with RCNN.” 2019. Web. 18 Nov 2019.

Vancouver:

Hijazi I. Animal ID Tag Recognition with Convolutional and Recurrent Neural Network : Identifying digits from a number sequence with RCNN. [Internet] [Thesis]. University of Skövde; 2019. [cited 2019 Nov 18]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17031.

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

Council of Science Editors:

Hijazi I. Animal ID Tag Recognition with Convolutional and Recurrent Neural Network : Identifying digits from a number sequence with RCNN. [Thesis]. University of Skövde; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17031

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


Brno University of Technology

25. Huf, Petr. Machine Learning Strategies in Electronic Trading .

Degree: 2014, Brno University of Technology

 Úspěšné obchodování na trzích je snem mnoha lidí. Zajímavým odvětvím tohoto byznysu je elektronické obchodování, kde obchodní strategie běží na počítači bez jakéhokoliv zásahu člověka.… (more)

Subjects/Keywords: neuronová síť; rekurentní neuronová síť; burza; obchodování; trh; profit; model; neural network; recurrent neural network; stock exchange; trading; market; profit; model

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

APA (6th Edition):

Huf, P. (2014). Machine Learning Strategies in Electronic Trading . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/56492

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):

Huf, Petr. “Machine Learning Strategies in Electronic Trading .” 2014. Thesis, Brno University of Technology. Accessed November 18, 2019. http://hdl.handle.net/11012/56492.

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

MLA Handbook (7th Edition):

Huf, Petr. “Machine Learning Strategies in Electronic Trading .” 2014. Web. 18 Nov 2019.

Vancouver:

Huf P. Machine Learning Strategies in Electronic Trading . [Internet] [Thesis]. Brno University of Technology; 2014. [cited 2019 Nov 18]. Available from: http://hdl.handle.net/11012/56492.

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

Council of Science Editors:

Huf P. Machine Learning Strategies in Electronic Trading . [Thesis]. Brno University of Technology; 2014. Available from: http://hdl.handle.net/11012/56492

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


University of Western Ontario

26. Wang, Xindi. Incorporating Figure Captions and Descriptive Text into Mesh Term Indexing: A Deep Learning Approach.

Degree: 2019, University of Western Ontario

 The exponential increase of available documents online makes document classification an important application in natural language processing. The goal of text classification is to automatically… (more)

Subjects/Keywords: text classification; MeSH term indexing; deep learning; convolutional neural network; recurrent neural network; attention model; Artificial Intelligence and Robotics

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APA (6th Edition):

Wang, X. (2019). Incorporating Figure Captions and Descriptive Text into Mesh Term Indexing: A Deep Learning Approach. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/6263

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, Xindi. “Incorporating Figure Captions and Descriptive Text into Mesh Term Indexing: A Deep Learning Approach.” 2019. Thesis, University of Western Ontario. Accessed November 18, 2019. https://ir.lib.uwo.ca/etd/6263.

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

MLA Handbook (7th Edition):

Wang, Xindi. “Incorporating Figure Captions and Descriptive Text into Mesh Term Indexing: A Deep Learning Approach.” 2019. Web. 18 Nov 2019.

Vancouver:

Wang X. Incorporating Figure Captions and Descriptive Text into Mesh Term Indexing: A Deep Learning Approach. [Internet] [Thesis]. University of Western Ontario; 2019. [cited 2019 Nov 18]. Available from: https://ir.lib.uwo.ca/etd/6263.

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. Incorporating Figure Captions and Descriptive Text into Mesh Term Indexing: A Deep Learning Approach. [Thesis]. University of Western Ontario; 2019. Available from: https://ir.lib.uwo.ca/etd/6263

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

27. Hannum, Andrew. RNN-Based Generation of Polyphonic Music and Jazz Improvisation.

Degree: MS, Computer Science, 2018, U of Denver

  This paper presents techniques developed for algorithmic composition of both polyphonic music, and of simulated jazz improvisation, using multiple novel data sources and the… (more)

Subjects/Keywords: Artificial intelligence; Generative music; Jazz improvisation; Machine learning; Neural network; Recurrent neural network; Artificial Intelligence and Robotics; Computer Sciences; Music Pedagogy

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APA (6th Edition):

Hannum, A. (2018). RNN-Based Generation of Polyphonic Music and Jazz Improvisation. (Thesis). U of Denver. Retrieved from https://digitalcommons.du.edu/etd/1532

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):

Hannum, Andrew. “RNN-Based Generation of Polyphonic Music and Jazz Improvisation.” 2018. Thesis, U of Denver. Accessed November 18, 2019. https://digitalcommons.du.edu/etd/1532.

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

MLA Handbook (7th Edition):

Hannum, Andrew. “RNN-Based Generation of Polyphonic Music and Jazz Improvisation.” 2018. Web. 18 Nov 2019.

Vancouver:

Hannum A. RNN-Based Generation of Polyphonic Music and Jazz Improvisation. [Internet] [Thesis]. U of Denver; 2018. [cited 2019 Nov 18]. Available from: https://digitalcommons.du.edu/etd/1532.

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

Council of Science Editors:

Hannum A. RNN-Based Generation of Polyphonic Music and Jazz Improvisation. [Thesis]. U of Denver; 2018. Available from: https://digitalcommons.du.edu/etd/1532

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


Brno University of Technology

28. Zvěřina, Lukáš. Predikce datového toku v počítačových sítích .

Degree: 2013, Brno University of Technology

 Předmětem této diplomové práce bylo seznámit se s problematikou predikce výskytu dat v počítačových sítích. Dále se tato práce zabývala síťovým provozem a analýzou jeho… (more)

Subjects/Keywords: Síťový provoz; dynamická alokace šířky pásma; predikce; rekurentní neuronová síť; Neural Network Toolbox; Network traffic; dynamic bandwidth allocation; prediction; recurrent neural network; Neural Network Toolbox

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

APA (6th Edition):

Zvěřina, L. (2013). Predikce datového toku v počítačových sítích . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/26771

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):

Zvěřina, Lukáš. “Predikce datového toku v počítačových sítích .” 2013. Thesis, Brno University of Technology. Accessed November 18, 2019. http://hdl.handle.net/11012/26771.

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

MLA Handbook (7th Edition):

Zvěřina, Lukáš. “Predikce datového toku v počítačových sítích .” 2013. Web. 18 Nov 2019.

Vancouver:

Zvěřina L. Predikce datového toku v počítačových sítích . [Internet] [Thesis]. Brno University of Technology; 2013. [cited 2019 Nov 18]. Available from: http://hdl.handle.net/11012/26771.

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

Council of Science Editors:

Zvěřina L. Predikce datového toku v počítačových sítích . [Thesis]. Brno University of Technology; 2013. Available from: http://hdl.handle.net/11012/26771

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


Brno University of Technology

29. Člupek, Vlastimil. Nelineární analýza a predikce síťového provozu .

Degree: 2012, Brno University of Technology

 Tato diplomová práce se zabývá síťovým provozem a analýzou jeho vlastností. V této práci jsou rozebrány možnosti predikce síťového provozu pomocí FARIMA modelu, teorii chaosu… (more)

Subjects/Keywords: Síťový provoz; predikce; rekurentní neuronová síť; Neural Network Toolbox; dynamická alokace šířky pásma.; Network traffic; prediction; recurrent neural network; Neural Network Toolbox; dynamic bandwidth allocation.

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

APA (6th Edition):

Člupek, V. (2012). Nelineární analýza a predikce síťového provozu . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/8976

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):

Člupek, Vlastimil. “Nelineární analýza a predikce síťového provozu .” 2012. Thesis, Brno University of Technology. Accessed November 18, 2019. http://hdl.handle.net/11012/8976.

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

MLA Handbook (7th Edition):

Člupek, Vlastimil. “Nelineární analýza a predikce síťového provozu .” 2012. Web. 18 Nov 2019.

Vancouver:

Člupek V. Nelineární analýza a predikce síťového provozu . [Internet] [Thesis]. Brno University of Technology; 2012. [cited 2019 Nov 18]. Available from: http://hdl.handle.net/11012/8976.

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

Council of Science Editors:

Člupek V. Nelineární analýza a predikce síťového provozu . [Thesis]. Brno University of Technology; 2012. Available from: http://hdl.handle.net/11012/8976

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

30. Ahrneteg, Jakob. Semantic Segmentation of Historical Document Images Using Recurrent Neural Networks.

Degree: 2019, , Department of Software Engineering

  Background. This thesis focuses on the task of historical document semantic segmentation with recurrent neural networks. Document semantic segmentation involves the segmentation of a… (more)

Subjects/Keywords: semantic segmentation; page segmentation; recurrent neural network; layout analysis; semantisk segmentering; dokument segmentering; recurrent neural network; layout analys; Computer Sciences; Datavetenskap (datalogi)

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

APA (6th Edition):

Ahrneteg, J. (2019). Semantic Segmentation of Historical Document Images Using Recurrent Neural Networks. (Thesis). , Department of Software Engineering. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18219

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):

Ahrneteg, Jakob. “Semantic Segmentation of Historical Document Images Using Recurrent Neural Networks.” 2019. Thesis, , Department of Software Engineering. Accessed November 18, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18219.

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

MLA Handbook (7th Edition):

Ahrneteg, Jakob. “Semantic Segmentation of Historical Document Images Using Recurrent Neural Networks.” 2019. Web. 18 Nov 2019.

Vancouver:

Ahrneteg J. Semantic Segmentation of Historical Document Images Using Recurrent Neural Networks. [Internet] [Thesis]. , Department of Software Engineering; 2019. [cited 2019 Nov 18]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18219.

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

Council of Science Editors:

Ahrneteg J. Semantic Segmentation of Historical Document Images Using Recurrent Neural Networks. [Thesis]. , Department of Software Engineering; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18219

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

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