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You searched for subject:(deep learning). Showing records 1 – 30 of 1357 total matches.

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California State Polytechnic University – Pomona

1. Shimpi, Shubhangi. Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients.

Degree: MS, Department of Computer Science, 2018, California State Polytechnic University – Pomona

 Electroencephalogram (EEG) data includes information of electrical activity of a brain; thus is commonly used to diagnose any underlying neurological condition such as epilepsy. Epileptic… (more)

Subjects/Keywords: deep learning

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APA (6th Edition):

Shimpi, S. (2018). Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients. (Masters Thesis). California State Polytechnic University – Pomona. Retrieved from http://hdl.handle.net/10211.3/199949

Chicago Manual of Style (16th Edition):

Shimpi, Shubhangi. “Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients.” 2018. Masters Thesis, California State Polytechnic University – Pomona. Accessed August 18, 2019. http://hdl.handle.net/10211.3/199949.

MLA Handbook (7th Edition):

Shimpi, Shubhangi. “Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients.” 2018. Web. 18 Aug 2019.

Vancouver:

Shimpi S. Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients. [Internet] [Masters thesis]. California State Polytechnic University – Pomona; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/10211.3/199949.

Council of Science Editors:

Shimpi S. Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients. [Masters Thesis]. California State Polytechnic University – Pomona; 2018. Available from: http://hdl.handle.net/10211.3/199949


University of Sydney

2. Windrim, Lloyd. Illumination Invariant Deep Learning for Hyperspectral Data .

Degree: 2018, University of Sydney

 Motivated by the variability in hyperspectral images due to illumination and the difficulty in acquiring labelled data, this thesis proposes different approaches for learning illumination… (more)

Subjects/Keywords: Deep learning

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APA (6th Edition):

Windrim, L. (2018). Illumination Invariant Deep Learning for Hyperspectral Data . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/18734

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):

Windrim, Lloyd. “Illumination Invariant Deep Learning for Hyperspectral Data .” 2018. Thesis, University of Sydney. Accessed August 18, 2019. http://hdl.handle.net/2123/18734.

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

MLA Handbook (7th Edition):

Windrim, Lloyd. “Illumination Invariant Deep Learning for Hyperspectral Data .” 2018. Web. 18 Aug 2019.

Vancouver:

Windrim L. Illumination Invariant Deep Learning for Hyperspectral Data . [Internet] [Thesis]. University of Sydney; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/2123/18734.

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

Council of Science Editors:

Windrim L. Illumination Invariant Deep Learning for Hyperspectral Data . [Thesis]. University of Sydney; 2018. Available from: http://hdl.handle.net/2123/18734

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


Oregon State University

3. Ghaeini, Mohammad Reza. Event Detection with Forward-Backward Recurrent Neural Networks.

Degree: MS, 2017, Oregon State University

 Automatic event extraction from natural text is an important and challenging task for natural language understanding. Traditional event detection methods heavily rely on manually engineered… (more)

Subjects/Keywords: Deep Learning

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APA (6th Edition):

Ghaeini, M. R. (2017). Event Detection with Forward-Backward Recurrent Neural Networks. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/61576

Chicago Manual of Style (16th Edition):

Ghaeini, Mohammad Reza. “Event Detection with Forward-Backward Recurrent Neural Networks.” 2017. Masters Thesis, Oregon State University. Accessed August 18, 2019. http://hdl.handle.net/1957/61576.

MLA Handbook (7th Edition):

Ghaeini, Mohammad Reza. “Event Detection with Forward-Backward Recurrent Neural Networks.” 2017. Web. 18 Aug 2019.

Vancouver:

Ghaeini MR. Event Detection with Forward-Backward Recurrent Neural Networks. [Internet] [Masters thesis]. Oregon State University; 2017. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/1957/61576.

Council of Science Editors:

Ghaeini MR. Event Detection with Forward-Backward Recurrent Neural Networks. [Masters Thesis]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61576


Cornell University

4. Sedra, Daniel. Training Paradigms For Deep Residual Networks .

Degree: 2016, Cornell University

 Convolutional networks are the current state of the art for image tasks. It has long been known that depth is key for increasing their expressive… (more)

Subjects/Keywords: deep learning; machine learning; deep residual networks

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APA (6th Edition):

Sedra, D. (2016). Training Paradigms For Deep Residual Networks . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/44294

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):

Sedra, Daniel. “Training Paradigms For Deep Residual Networks .” 2016. Thesis, Cornell University. Accessed August 18, 2019. http://hdl.handle.net/1813/44294.

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

MLA Handbook (7th Edition):

Sedra, Daniel. “Training Paradigms For Deep Residual Networks .” 2016. Web. 18 Aug 2019.

Vancouver:

Sedra D. Training Paradigms For Deep Residual Networks . [Internet] [Thesis]. Cornell University; 2016. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/1813/44294.

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

Council of Science Editors:

Sedra D. Training Paradigms For Deep Residual Networks . [Thesis]. Cornell University; 2016. Available from: http://hdl.handle.net/1813/44294

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


Georgia Tech

5. Choi, Edward. Doctor AI: Interpretable deep learning for modeling electronic health records.

Degree: PhD, Computational Science and Engineering, 2018, Georgia Tech

Deep learning recently has been showing superior performance in complex domains such as computer vision, audio processing and natural language processing compared to traditional statistical… (more)

Subjects/Keywords: Deep learning; Healthcare

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APA (6th Edition):

Choi, E. (2018). Doctor AI: Interpretable deep learning for modeling electronic health records. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60226

Chicago Manual of Style (16th Edition):

Choi, Edward. “Doctor AI: Interpretable deep learning for modeling electronic health records.” 2018. Doctoral Dissertation, Georgia Tech. Accessed August 18, 2019. http://hdl.handle.net/1853/60226.

MLA Handbook (7th Edition):

Choi, Edward. “Doctor AI: Interpretable deep learning for modeling electronic health records.” 2018. Web. 18 Aug 2019.

Vancouver:

Choi E. Doctor AI: Interpretable deep learning for modeling electronic health records. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/1853/60226.

Council of Science Editors:

Choi E. Doctor AI: Interpretable deep learning for modeling electronic health records. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60226


University of KwaZulu-Natal

6. Govender, Lishen. Determination of quantum entanglement concurrence using multilayer perceptron neural networks.

Degree: 2017, University of KwaZulu-Natal

 Artificial Neural Networks, inspired by biological neural networks, have seen widespread implementations across all research areas in the past few years. This partly due to… (more)

Subjects/Keywords: Deep learning.; Machine learning.

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APA (6th Edition):

Govender, L. (2017). Determination of quantum entanglement concurrence using multilayer perceptron neural networks. (Thesis). University of KwaZulu-Natal. Retrieved from http://hdl.handle.net/10413/15713

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):

Govender, Lishen. “Determination of quantum entanglement concurrence using multilayer perceptron neural networks.” 2017. Thesis, University of KwaZulu-Natal. Accessed August 18, 2019. http://hdl.handle.net/10413/15713.

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

MLA Handbook (7th Edition):

Govender, Lishen. “Determination of quantum entanglement concurrence using multilayer perceptron neural networks.” 2017. Web. 18 Aug 2019.

Vancouver:

Govender L. Determination of quantum entanglement concurrence using multilayer perceptron neural networks. [Internet] [Thesis]. University of KwaZulu-Natal; 2017. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/10413/15713.

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

Council of Science Editors:

Govender L. Determination of quantum entanglement concurrence using multilayer perceptron neural networks. [Thesis]. University of KwaZulu-Natal; 2017. Available from: http://hdl.handle.net/10413/15713

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


California State University – Sacramento

7. Poosarla, Akshay. Bone age prediction with convolutional neural networks.

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

 Skeletal bone age assessment is a common clinical practice to analyze and assess the biological maturity of pediatric patients. This process generally involves taking X-ray… (more)

Subjects/Keywords: Machine learning; Deep learning; Boneage

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APA (6th Edition):

Poosarla, A. (2019). Bone age prediction with convolutional neural networks. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/207660

Chicago Manual of Style (16th Edition):

Poosarla, Akshay. “Bone age prediction with convolutional neural networks.” 2019. Masters Thesis, California State University – Sacramento. Accessed August 18, 2019. http://hdl.handle.net/10211.3/207660.

MLA Handbook (7th Edition):

Poosarla, Akshay. “Bone age prediction with convolutional neural networks.” 2019. Web. 18 Aug 2019.

Vancouver:

Poosarla A. Bone age prediction with convolutional neural networks. [Internet] [Masters thesis]. California State University – Sacramento; 2019. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/10211.3/207660.

Council of Science Editors:

Poosarla A. Bone age prediction with convolutional neural networks. [Masters Thesis]. California State University – Sacramento; 2019. Available from: http://hdl.handle.net/10211.3/207660


Cornell University

8. Lenz, Ian. Deep Learning For Robotics .

Degree: 2016, Cornell University

 Robotics faces many unique challenges as robotic platforms move out of the lab and into the real world. In particular, the huge amount of variety… (more)

Subjects/Keywords: Robotics; Machine learning; Deep learning

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APA (6th Edition):

Lenz, I. (2016). Deep Learning For Robotics . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/44317

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):

Lenz, Ian. “Deep Learning For Robotics .” 2016. Thesis, Cornell University. Accessed August 18, 2019. http://hdl.handle.net/1813/44317.

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

MLA Handbook (7th Edition):

Lenz, Ian. “Deep Learning For Robotics .” 2016. Web. 18 Aug 2019.

Vancouver:

Lenz I. Deep Learning For Robotics . [Internet] [Thesis]. Cornell University; 2016. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/1813/44317.

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

Council of Science Editors:

Lenz I. Deep Learning For Robotics . [Thesis]. Cornell University; 2016. Available from: http://hdl.handle.net/1813/44317

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


University of Illinois – Urbana-Champaign

9. Deshpande, Ishan. Generative modeling using the sliced Wasserstein distance.

Degree: MS, Electrical & Computer Engr, 2018, University of Illinois – Urbana-Champaign

 Generative adversarial nets (GANs) are very successful at modeling distributions from given samples, even in the high-dimensional case. However, their formulation is also known to… (more)

Subjects/Keywords: Machine Learning; Deep Learning

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APA (6th Edition):

Deshpande, I. (2018). Generative modeling using the sliced Wasserstein distance. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/100951

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):

Deshpande, Ishan. “Generative modeling using the sliced Wasserstein distance.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed August 18, 2019. http://hdl.handle.net/2142/100951.

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

MLA Handbook (7th Edition):

Deshpande, Ishan. “Generative modeling using the sliced Wasserstein distance.” 2018. Web. 18 Aug 2019.

Vancouver:

Deshpande I. Generative modeling using the sliced Wasserstein distance. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/2142/100951.

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

Council of Science Editors:

Deshpande I. Generative modeling using the sliced Wasserstein distance. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/100951

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


Australian National University

10. Dong, Cong. Spatial Deep Networks for Outdoor Scene Classification .

Degree: 2015, Australian National University

 Scene classification has become an increasingly popular topic in computer vision. The techniques for scene classification can be widely used in many other aspects, such… (more)

Subjects/Keywords: Deep Learning; Scene Classification; Spatial Deep Networks

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APA (6th Edition):

Dong, C. (2015). Spatial Deep Networks for Outdoor Scene Classification . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/101712

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):

Dong, Cong. “Spatial Deep Networks for Outdoor Scene Classification .” 2015. Thesis, Australian National University. Accessed August 18, 2019. http://hdl.handle.net/1885/101712.

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

MLA Handbook (7th Edition):

Dong, Cong. “Spatial Deep Networks for Outdoor Scene Classification .” 2015. Web. 18 Aug 2019.

Vancouver:

Dong C. Spatial Deep Networks for Outdoor Scene Classification . [Internet] [Thesis]. Australian National University; 2015. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/1885/101712.

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

Council of Science Editors:

Dong C. Spatial Deep Networks for Outdoor Scene Classification . [Thesis]. Australian National University; 2015. Available from: http://hdl.handle.net/1885/101712

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


University of Texas – Austin

11. Hausknecht, Matthew John. Cooperation and communication in multiagent deep reinforcement learning.

Degree: Computer Sciences, 2016, University of Texas – Austin

 Reinforcement learning is the area of machine learning concerned with learning which actions to execute in an unknown environment in order to maximize cumulative reward.… (more)

Subjects/Keywords: Reinforcement learning; Deep learning; Multiagent learning; Cooperation; Communication; RoboCup; POMDP; Deep reinforcement learning; Deep RL

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APA (6th Edition):

Hausknecht, M. J. (2016). Cooperation and communication in multiagent deep reinforcement learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/45681

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):

Hausknecht, Matthew John. “Cooperation and communication in multiagent deep reinforcement learning.” 2016. Thesis, University of Texas – Austin. Accessed August 18, 2019. http://hdl.handle.net/2152/45681.

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

MLA Handbook (7th Edition):

Hausknecht, Matthew John. “Cooperation and communication in multiagent deep reinforcement learning.” 2016. Web. 18 Aug 2019.

Vancouver:

Hausknecht MJ. Cooperation and communication in multiagent deep reinforcement learning. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/2152/45681.

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

Council of Science Editors:

Hausknecht MJ. Cooperation and communication in multiagent deep reinforcement learning. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/45681

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


University of Guelph

12. Im, Jiwoong. Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems .

Degree: 2015, University of Guelph

 The objective of this thesis is to take the dynamical systems approach to understand the unsupervised learning models and learning algorithms. Gated auto-encoders (GAEs) are… (more)

Subjects/Keywords: Machine learning; Deep Learning; unsupervised learning

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APA (6th Edition):

Im, J. (2015). Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809

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):

Im, Jiwoong. “Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems .” 2015. Thesis, University of Guelph. Accessed August 18, 2019. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809.

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

MLA Handbook (7th Edition):

Im, Jiwoong. “Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems .” 2015. Web. 18 Aug 2019.

Vancouver:

Im J. Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems . [Internet] [Thesis]. University of Guelph; 2015. [cited 2019 Aug 18]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809.

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

Council of Science Editors:

Im J. Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems . [Thesis]. University of Guelph; 2015. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809

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


NSYSU

13. Lin, Kun-da. Deep Reinforcement Learning with a Gating Network.

Degree: Master, Electrical Engineering, 2017, NSYSU

 Reinforcement Learning (RL) is a good way to train the robot since it doesn't need an exact model of the environment. All need is to… (more)

Subjects/Keywords: Reinforcement Learning; Deep Reinforcement Learning; Deep Learning; Gating network; Neural network

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APA (6th Edition):

Lin, K. (2017). Deep Reinforcement Learning with a Gating Network. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536

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, Kun-da. “Deep Reinforcement Learning with a Gating Network.” 2017. Thesis, NSYSU. Accessed August 18, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536.

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

MLA Handbook (7th Edition):

Lin, Kun-da. “Deep Reinforcement Learning with a Gating Network.” 2017. Web. 18 Aug 2019.

Vancouver:

Lin K. Deep Reinforcement Learning with a Gating Network. [Internet] [Thesis]. NSYSU; 2017. [cited 2019 Aug 18]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536.

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

Council of Science Editors:

Lin K. Deep Reinforcement Learning with a Gating Network. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536

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


Penn State University

14. Lageman, Nathaniel John. BinDNN: Resilient Function Matching Using Deep Learning.

Degree: MS, Computer Science and Engineering, 2016, Penn State University

 Determining if two functions taken from different compiled binaries originate from the same function in the source code has many applications to malware reverse engineering.… (more)

Subjects/Keywords: reverse engineering; malware; deep learning

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APA (6th Edition):

Lageman, N. J. (2016). BinDNN: Resilient Function Matching Using Deep Learning. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/12477njl5114

Chicago Manual of Style (16th Edition):

Lageman, Nathaniel John. “BinDNN: Resilient Function Matching Using Deep Learning.” 2016. Masters Thesis, Penn State University. Accessed August 18, 2019. https://etda.libraries.psu.edu/catalog/12477njl5114.

MLA Handbook (7th Edition):

Lageman, Nathaniel John. “BinDNN: Resilient Function Matching Using Deep Learning.” 2016. Web. 18 Aug 2019.

Vancouver:

Lageman NJ. BinDNN: Resilient Function Matching Using Deep Learning. [Internet] [Masters thesis]. Penn State University; 2016. [cited 2019 Aug 18]. Available from: https://etda.libraries.psu.edu/catalog/12477njl5114.

Council of Science Editors:

Lageman NJ. BinDNN: Resilient Function Matching Using Deep Learning. [Masters Thesis]. Penn State University; 2016. Available from: https://etda.libraries.psu.edu/catalog/12477njl5114


Penn State University

15. Papernot, Nicolas. On The Integrity Of Deep Learning Systems In Adversarial Settings.

Degree: MS, Computer Science and Engineering, 2016, Penn State University

Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, imperfections in the… (more)

Subjects/Keywords: computer security; deep learning

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APA (6th Edition):

Papernot, N. (2016). On The Integrity Of Deep Learning Systems In Adversarial Settings. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/28680

Chicago Manual of Style (16th Edition):

Papernot, Nicolas. “On The Integrity Of Deep Learning Systems In Adversarial Settings.” 2016. Masters Thesis, Penn State University. Accessed August 18, 2019. https://etda.libraries.psu.edu/catalog/28680.

MLA Handbook (7th Edition):

Papernot, Nicolas. “On The Integrity Of Deep Learning Systems In Adversarial Settings.” 2016. Web. 18 Aug 2019.

Vancouver:

Papernot N. On The Integrity Of Deep Learning Systems In Adversarial Settings. [Internet] [Masters thesis]. Penn State University; 2016. [cited 2019 Aug 18]. Available from: https://etda.libraries.psu.edu/catalog/28680.

Council of Science Editors:

Papernot N. On The Integrity Of Deep Learning Systems In Adversarial Settings. [Masters Thesis]. Penn State University; 2016. Available from: https://etda.libraries.psu.edu/catalog/28680


University of Manchester

16. Salman, Ahmad. Learning speaker-specific characteristics with deep neural architecture.

Degree: PhD, 2012, University of Manchester

 Robust Speaker Recognition (SR) has been a focus of attention for researchers since long. The advancement in speech-aided technologies especially biometrics highlights the necessity of… (more)

Subjects/Keywords: 006.4; Speaker Recognition; Deep Learning

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APA (6th Edition):

Salman, A. (2012). Learning speaker-specific characteristics with deep neural architecture. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/learning-speakerspecific-characteristics-with-deep-neural-architecture(24acb31d-2106-4e52-80ab-6c649838026a).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558060

Chicago Manual of Style (16th Edition):

Salman, Ahmad. “Learning speaker-specific characteristics with deep neural architecture.” 2012. Doctoral Dissertation, University of Manchester. Accessed August 18, 2019. https://www.research.manchester.ac.uk/portal/en/theses/learning-speakerspecific-characteristics-with-deep-neural-architecture(24acb31d-2106-4e52-80ab-6c649838026a).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558060.

MLA Handbook (7th Edition):

Salman, Ahmad. “Learning speaker-specific characteristics with deep neural architecture.” 2012. Web. 18 Aug 2019.

Vancouver:

Salman A. Learning speaker-specific characteristics with deep neural architecture. [Internet] [Doctoral dissertation]. University of Manchester; 2012. [cited 2019 Aug 18]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/learning-speakerspecific-characteristics-with-deep-neural-architecture(24acb31d-2106-4e52-80ab-6c649838026a).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558060.

Council of Science Editors:

Salman A. Learning speaker-specific characteristics with deep neural architecture. [Doctoral Dissertation]. University of Manchester; 2012. Available from: https://www.research.manchester.ac.uk/portal/en/theses/learning-speakerspecific-characteristics-with-deep-neural-architecture(24acb31d-2106-4e52-80ab-6c649838026a).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558060


University of Otago

17. Szymanski, Lech. Deep architectures and classification by intermediary transformations .

Degree: 2012, University of Otago

 With the development of deep belief nets, the empirical evidence supporting a link between deep architecture neural networks and generalisation with respect to classification has… (more)

Subjects/Keywords: Machine learning; Classification; Deep architectures

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

APA (6th Edition):

Szymanski, L. (2012). Deep architectures and classification by intermediary transformations . (Doctoral Dissertation). University of Otago. Retrieved from http://hdl.handle.net/10523/2129

Chicago Manual of Style (16th Edition):

Szymanski, Lech. “Deep architectures and classification by intermediary transformations .” 2012. Doctoral Dissertation, University of Otago. Accessed August 18, 2019. http://hdl.handle.net/10523/2129.

MLA Handbook (7th Edition):

Szymanski, Lech. “Deep architectures and classification by intermediary transformations .” 2012. Web. 18 Aug 2019.

Vancouver:

Szymanski L. Deep architectures and classification by intermediary transformations . [Internet] [Doctoral dissertation]. University of Otago; 2012. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/10523/2129.

Council of Science Editors:

Szymanski L. Deep architectures and classification by intermediary transformations . [Doctoral Dissertation]. University of Otago; 2012. Available from: http://hdl.handle.net/10523/2129


University of Houston

18. Shah, Tanay Jignesh. Multi-Purpose Chest X-Ray Analytics System Using Deep Learning Techniques.

Degree: Electrical and Computer Engineering, Department of, 2018, University of Houston

 There has been rapid and tremendous progress in the past few years in the field of deep learning, mainly due to the availability of computational… (more)

Subjects/Keywords: chest X-rays; deep learning

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APA (6th Edition):

Shah, T. J. (2018). Multi-Purpose Chest X-Ray Analytics System Using Deep Learning Techniques. (Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3459

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):

Shah, Tanay Jignesh. “Multi-Purpose Chest X-Ray Analytics System Using Deep Learning Techniques.” 2018. Thesis, University of Houston. Accessed August 18, 2019. http://hdl.handle.net/10657/3459.

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

MLA Handbook (7th Edition):

Shah, Tanay Jignesh. “Multi-Purpose Chest X-Ray Analytics System Using Deep Learning Techniques.” 2018. Web. 18 Aug 2019.

Vancouver:

Shah TJ. Multi-Purpose Chest X-Ray Analytics System Using Deep Learning Techniques. [Internet] [Thesis]. University of Houston; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/10657/3459.

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

Council of Science Editors:

Shah TJ. Multi-Purpose Chest X-Ray Analytics System Using Deep Learning Techniques. [Thesis]. University of Houston; 2018. Available from: http://hdl.handle.net/10657/3459

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

19. Furusho, Yasutaka. Roles of Pre-training in Deep Neural Networks from Information Theoretical Perspective : Pre-trainingがニューラルネットワークに与える影響の情報理論的解析; Pre-training ガ ニューラル ネットワーク ニ アタエル エイキョウ ノ ジョウホウ リロンテキ カイセキ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

Subjects/Keywords: deep learning

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

APA (6th Edition):

Furusho, Y. (n.d.). Roles of Pre-training in Deep Neural Networks from Information Theoretical Perspective : Pre-trainingがニューラルネットワークに与える影響の情報理論的解析; Pre-training ガ ニューラル ネットワーク ニ アタエル エイキョウ ノ ジョウホウ リロンテキ カイセキ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/11622

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Furusho, Yasutaka. “Roles of Pre-training in Deep Neural Networks from Information Theoretical Perspective : Pre-trainingがニューラルネットワークに与える影響の情報理論的解析; Pre-training ガ ニューラル ネットワーク ニ アタエル エイキョウ ノ ジョウホウ リロンテキ カイセキ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed August 18, 2019. http://hdl.handle.net/10061/11622.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Furusho, Yasutaka. “Roles of Pre-training in Deep Neural Networks from Information Theoretical Perspective : Pre-trainingがニューラルネットワークに与える影響の情報理論的解析; Pre-training ガ ニューラル ネットワーク ニ アタエル エイキョウ ノ ジョウホウ リロンテキ カイセキ.” Web. 18 Aug 2019.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Furusho Y. Roles of Pre-training in Deep Neural Networks from Information Theoretical Perspective : Pre-trainingがニューラルネットワークに与える影響の情報理論的解析; Pre-training ガ ニューラル ネットワーク ニ アタエル エイキョウ ノ ジョウホウ リロンテキ カイセキ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2019 Aug 18]. Available from: http://hdl.handle.net/10061/11622.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Furusho Y. Roles of Pre-training in Deep Neural Networks from Information Theoretical Perspective : Pre-trainingがニューラルネットワークに与える影響の情報理論的解析; Pre-training ガ ニューラル ネットワーク ニ アタエル エイキョウ ノ ジョウホウ リロンテキ カイセキ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/11622

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

20. Alkittawi, Hend. A deep-learning-based fall-detection system to support aging-in-place .

Degree: 2017, Texas A&M University – Corpus Christi

 Emergency departments treat around 2.5 million older people for fall injuries each year. Serious head and broken bones injuries occur in 20% of falls. Fall… (more)

Subjects/Keywords: deep-learning; fall-detection

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

APA (6th Edition):

Alkittawi, H. (2017). A deep-learning-based fall-detection system to support aging-in-place . (Thesis). Texas A&M University – Corpus Christi. Retrieved from http://hdl.handle.net/1969.6/2975

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):

Alkittawi, Hend. “A deep-learning-based fall-detection system to support aging-in-place .” 2017. Thesis, Texas A&M University – Corpus Christi. Accessed August 18, 2019. http://hdl.handle.net/1969.6/2975.

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

MLA Handbook (7th Edition):

Alkittawi, Hend. “A deep-learning-based fall-detection system to support aging-in-place .” 2017. Web. 18 Aug 2019.

Vancouver:

Alkittawi H. A deep-learning-based fall-detection system to support aging-in-place . [Internet] [Thesis]. Texas A&M University – Corpus Christi; 2017. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/1969.6/2975.

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

Council of Science Editors:

Alkittawi H. A deep-learning-based fall-detection system to support aging-in-place . [Thesis]. Texas A&M University – Corpus Christi; 2017. Available from: http://hdl.handle.net/1969.6/2975

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


University of Illinois – Urbana-Champaign

21. Wang, Zhangyang. Task-specific and interpretable feature learning.

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

Deep learning models have had tremendous impacts in recent years, while a question has been raised by many: Is deep learning just a triumph of… (more)

Subjects/Keywords: deep learning; sparse representation

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APA (6th Edition):

Wang, Z. (2016). Task-specific and interpretable feature learning. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/95560

Chicago Manual of Style (16th Edition):

Wang, Zhangyang. “Task-specific and interpretable feature learning.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed August 18, 2019. http://hdl.handle.net/2142/95560.

MLA Handbook (7th Edition):

Wang, Zhangyang. “Task-specific and interpretable feature learning.” 2016. Web. 18 Aug 2019.

Vancouver:

Wang Z. Task-specific and interpretable feature learning. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/2142/95560.

Council of Science Editors:

Wang Z. Task-specific and interpretable feature learning. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/95560


McMaster University

22. Liu, Zheng. Generic Model-Agnostic Convolutional Neural Networks for Single Image Dehazing.

Degree: MASc, 2018, McMaster University

Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis. In this paper, I propose an end-to-end generative… (more)

Subjects/Keywords: image dehazing; deep learning

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APA (6th Edition):

Liu, Z. (2018). Generic Model-Agnostic Convolutional Neural Networks for Single Image Dehazing. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/23979

Chicago Manual of Style (16th Edition):

Liu, Zheng. “Generic Model-Agnostic Convolutional Neural Networks for Single Image Dehazing.” 2018. Masters Thesis, McMaster University. Accessed August 18, 2019. http://hdl.handle.net/11375/23979.

MLA Handbook (7th Edition):

Liu, Zheng. “Generic Model-Agnostic Convolutional Neural Networks for Single Image Dehazing.” 2018. Web. 18 Aug 2019.

Vancouver:

Liu Z. Generic Model-Agnostic Convolutional Neural Networks for Single Image Dehazing. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/11375/23979.

Council of Science Editors:

Liu Z. Generic Model-Agnostic Convolutional Neural Networks for Single Image Dehazing. [Masters Thesis]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/23979


University of Adelaide

23. Li, Teng. Deep learning for fine-grained visual recognition.

Degree: 2017, University of Adelaide

 Fine-grained object recognition is an important task in computer vision. The cross-convolutional-layer pooling method is one of the significant milestones in the development of this… (more)

Subjects/Keywords: deep learning; fine-grained; recognition

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APA (6th Edition):

Li, T. (2017). Deep learning for fine-grained visual recognition. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/106421

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, Teng. “Deep learning for fine-grained visual recognition.” 2017. Thesis, University of Adelaide. Accessed August 18, 2019. http://hdl.handle.net/2440/106421.

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

MLA Handbook (7th Edition):

Li, Teng. “Deep learning for fine-grained visual recognition.” 2017. Web. 18 Aug 2019.

Vancouver:

Li T. Deep learning for fine-grained visual recognition. [Internet] [Thesis]. University of Adelaide; 2017. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/2440/106421.

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

Council of Science Editors:

Li T. Deep learning for fine-grained visual recognition. [Thesis]. University of Adelaide; 2017. Available from: http://hdl.handle.net/2440/106421

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


McMaster University

24. Chi, Zhixiang. IMAGE RESTORATIONS USING DEEP LEARNING TECHNIQUES.

Degree: MASc, 2018, McMaster University

Conventional methods for solving image restoration problems are typically built on an image degradation model and on some priors of the latent image. The model… (more)

Subjects/Keywords: Image restoration; Deep learning

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APA (6th Edition):

Chi, Z. (2018). IMAGE RESTORATIONS USING DEEP LEARNING TECHNIQUES. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/24290

Chicago Manual of Style (16th Edition):

Chi, Zhixiang. “IMAGE RESTORATIONS USING DEEP LEARNING TECHNIQUES.” 2018. Masters Thesis, McMaster University. Accessed August 18, 2019. http://hdl.handle.net/11375/24290.

MLA Handbook (7th Edition):

Chi, Zhixiang. “IMAGE RESTORATIONS USING DEEP LEARNING TECHNIQUES.” 2018. Web. 18 Aug 2019.

Vancouver:

Chi Z. IMAGE RESTORATIONS USING DEEP LEARNING TECHNIQUES. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/11375/24290.

Council of Science Editors:

Chi Z. IMAGE RESTORATIONS USING DEEP LEARNING TECHNIQUES. [Masters Thesis]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/24290


Indiana University

25. Sanders, Craig Aaron. Using deep learning to automatically extract psychological representations of complex natural stimuli .

Degree: 2018, Indiana University

 Cognitive psychologists have developed many formal models of categorization, but they have been almost exclusively tested using artificial categories because deriving psychological representations of natural… (more)

Subjects/Keywords: representations; deep learning; categorization

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APA (6th Edition):

Sanders, C. A. (2018). Using deep learning to automatically extract psychological representations of complex natural stimuli . (Thesis). Indiana University. Retrieved from http://hdl.handle.net/2022/22415

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):

Sanders, Craig Aaron. “Using deep learning to automatically extract psychological representations of complex natural stimuli .” 2018. Thesis, Indiana University. Accessed August 18, 2019. http://hdl.handle.net/2022/22415.

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

MLA Handbook (7th Edition):

Sanders, Craig Aaron. “Using deep learning to automatically extract psychological representations of complex natural stimuli .” 2018. Web. 18 Aug 2019.

Vancouver:

Sanders CA. Using deep learning to automatically extract psychological representations of complex natural stimuli . [Internet] [Thesis]. Indiana University; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/2022/22415.

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

Council of Science Editors:

Sanders CA. Using deep learning to automatically extract psychological representations of complex natural stimuli . [Thesis]. Indiana University; 2018. Available from: http://hdl.handle.net/2022/22415

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


University of Texas – Austin

26. -5777-2824. A study of generative adversarial networks and possible extensions of GANs.

Degree: Electrical and Computer Engineering, 2017, University of Texas – Austin

 The goal of our research is to explore the power of generative adversarial networks (GANs). We take a review of deep learning and many extended… (more)

Subjects/Keywords: Deep learning; Generative adversarial networks

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APA (6th Edition):

-5777-2824. (2017). A study of generative adversarial networks and possible extensions of GANs. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61664

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

Chicago Manual of Style (16th Edition):

-5777-2824. “A study of generative adversarial networks and possible extensions of GANs.” 2017. Thesis, University of Texas – Austin. Accessed August 18, 2019. http://hdl.handle.net/2152/61664.

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

MLA Handbook (7th Edition):

-5777-2824. “A study of generative adversarial networks and possible extensions of GANs.” 2017. Web. 18 Aug 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-5777-2824. A study of generative adversarial networks and possible extensions of GANs. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/2152/61664.

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

Council of Science Editors:

-5777-2824. A study of generative adversarial networks and possible extensions of GANs. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61664

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


University of Waterloo

27. Tang, Yichuan. Robust Visual Recognition Using Multilayer Generative Neural Networks.

Degree: 2010, University of Waterloo

Deep generative neural networks such as the Deep Belief Network and Deep Boltzmann Machines have been used successfully to model high dimensional visual data. However,… (more)

Subjects/Keywords: Neural Networks; Deep Learning

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APA (6th Edition):

Tang, Y. (2010). Robust Visual Recognition Using Multilayer Generative Neural Networks. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/5376

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):

Tang, Yichuan. “Robust Visual Recognition Using Multilayer Generative Neural Networks.” 2010. Thesis, University of Waterloo. Accessed August 18, 2019. http://hdl.handle.net/10012/5376.

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

MLA Handbook (7th Edition):

Tang, Yichuan. “Robust Visual Recognition Using Multilayer Generative Neural Networks.” 2010. Web. 18 Aug 2019.

Vancouver:

Tang Y. Robust Visual Recognition Using Multilayer Generative Neural Networks. [Internet] [Thesis]. University of Waterloo; 2010. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/10012/5376.

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

Council of Science Editors:

Tang Y. Robust Visual Recognition Using Multilayer Generative Neural Networks. [Thesis]. University of Waterloo; 2010. Available from: http://hdl.handle.net/10012/5376

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


UCLA

28. JI, NAN. Incomplete Image Filling by Popular Deep Learning Methods.

Degree: Statistics, 2019, UCLA

 Incomplete image filling task, often known as the image inpainting task, is a popular topic in the applied deep learning field. This thesis paper considers… (more)

Subjects/Keywords: Statistics; Deep Learning; Image Inpainting

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APA (6th Edition):

JI, N. (2019). Incomplete Image Filling by Popular Deep Learning Methods. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/1tz4d61x

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):

JI, NAN. “Incomplete Image Filling by Popular Deep Learning Methods.” 2019. Thesis, UCLA. Accessed August 18, 2019. http://www.escholarship.org/uc/item/1tz4d61x.

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

MLA Handbook (7th Edition):

JI, NAN. “Incomplete Image Filling by Popular Deep Learning Methods.” 2019. Web. 18 Aug 2019.

Vancouver:

JI N. Incomplete Image Filling by Popular Deep Learning Methods. [Internet] [Thesis]. UCLA; 2019. [cited 2019 Aug 18]. Available from: http://www.escholarship.org/uc/item/1tz4d61x.

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

Council of Science Editors:

JI N. Incomplete Image Filling by Popular Deep Learning Methods. [Thesis]. UCLA; 2019. Available from: http://www.escholarship.org/uc/item/1tz4d61x

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


University of Illinois – Urbana-Champaign

29. Paine, Thomas Le. Practical considerations for deep learning.

Degree: PhD, Informatics, 2017, University of Illinois – Urbana-Champaign

 The work in this dissertation was done as a major shift in machine perception and deep learning research was happening. Neural networks have proved to… (more)

Subjects/Keywords: Deep learning; Computer vision

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APA (6th Edition):

Paine, T. L. (2017). Practical considerations for deep learning. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/97374

Chicago Manual of Style (16th Edition):

Paine, Thomas Le. “Practical considerations for deep learning.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed August 18, 2019. http://hdl.handle.net/2142/97374.

MLA Handbook (7th Edition):

Paine, Thomas Le. “Practical considerations for deep learning.” 2017. Web. 18 Aug 2019.

Vancouver:

Paine TL. Practical considerations for deep learning. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/2142/97374.

Council of Science Editors:

Paine TL. Practical considerations for deep learning. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/97374


University of Illinois – Urbana-Champaign

30. Liang, Shiyu. Why deep neural networks for function approximation.

Degree: MS, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

 Recently there has been much interest in understanding why deep neural networks are preferred to shallow networks. We show that, for a large class of… (more)

Subjects/Keywords: Neural networks; Deep learning

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APA (6th Edition):

Liang, S. (2017). Why deep neural networks for function approximation. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99417

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):

Liang, Shiyu. “Why deep neural networks for function approximation.” 2017. Thesis, University of Illinois – Urbana-Champaign. Accessed August 18, 2019. http://hdl.handle.net/2142/99417.

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

MLA Handbook (7th Edition):

Liang, Shiyu. “Why deep neural networks for function approximation.” 2017. Web. 18 Aug 2019.

Vancouver:

Liang S. Why deep neural networks for function approximation. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2017. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/2142/99417.

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

Council of Science Editors:

Liang S. Why deep neural networks for function approximation. [Thesis]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99417

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

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