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You searched for subject:(Deep learning). Showing records 1 – 30 of 3078 total matches.

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Oregon State University

1. 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 February 27, 2021. http://hdl.handle.net/1957/61576.

MLA Handbook (7th Edition):

Ghaeini, Mohammad Reza. “Event Detection with Forward-Backward Recurrent Neural Networks.” 2017. Web. 27 Feb 2021.

Vancouver:

Ghaeini MR. Event Detection with Forward-Backward Recurrent Neural Networks. [Internet] [Masters thesis]. Oregon State University; 2017. [cited 2021 Feb 27]. 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


California State Polytechnic University – Pomona

2. Frank, Hakeem. Gaussian Process Models for Computer Vision.

Degree: MS, Department of Mathematics and Statistics, 2020, California State Polytechnic University – Pomona

 Supervised learning is the task of finding a function f(x) that maps an input x to an output y using observed data. Gaussian process models… (more)

Subjects/Keywords: deep learning

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APA (6th Edition):

Frank, H. (2020). Gaussian Process Models for Computer Vision. (Masters Thesis). California State Polytechnic University – Pomona. Retrieved from http://hdl.handle.net/10211.3/216857

Chicago Manual of Style (16th Edition):

Frank, Hakeem. “Gaussian Process Models for Computer Vision.” 2020. Masters Thesis, California State Polytechnic University – Pomona. Accessed February 27, 2021. http://hdl.handle.net/10211.3/216857.

MLA Handbook (7th Edition):

Frank, Hakeem. “Gaussian Process Models for Computer Vision.” 2020. Web. 27 Feb 2021.

Vancouver:

Frank H. Gaussian Process Models for Computer Vision. [Internet] [Masters thesis]. California State Polytechnic University – Pomona; 2020. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10211.3/216857.

Council of Science Editors:

Frank H. Gaussian Process Models for Computer Vision. [Masters Thesis]. California State Polytechnic University – Pomona; 2020. Available from: http://hdl.handle.net/10211.3/216857


Universidad de Cantabria

3. Noriega Puente, Andrea. Segmentación de gliomas en imagen de resonancia magnética multimodal: Glioma segmentation in multimodal magnetic resonance imaging.

Degree: Máster en Ciencia de Datos, 2019, Universidad de Cantabria

 RESUMEN: El glioma es el tipo de tumor cerebral más común, presentando distintos grados de malignidad y agresividad, así como un pronóstico variable. La gran… (more)

Subjects/Keywords: Deep Learning

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APA (6th Edition):

Noriega Puente, A. (2019). Segmentación de gliomas en imagen de resonancia magnética multimodal: Glioma segmentation in multimodal magnetic resonance imaging. (Masters Thesis). Universidad de Cantabria. Retrieved from http://hdl.handle.net/10902/17859

Chicago Manual of Style (16th Edition):

Noriega Puente, Andrea. “Segmentación de gliomas en imagen de resonancia magnética multimodal: Glioma segmentation in multimodal magnetic resonance imaging.” 2019. Masters Thesis, Universidad de Cantabria. Accessed February 27, 2021. http://hdl.handle.net/10902/17859.

MLA Handbook (7th Edition):

Noriega Puente, Andrea. “Segmentación de gliomas en imagen de resonancia magnética multimodal: Glioma segmentation in multimodal magnetic resonance imaging.” 2019. Web. 27 Feb 2021.

Vancouver:

Noriega Puente A. Segmentación de gliomas en imagen de resonancia magnética multimodal: Glioma segmentation in multimodal magnetic resonance imaging. [Internet] [Masters thesis]. Universidad de Cantabria; 2019. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10902/17859.

Council of Science Editors:

Noriega Puente A. Segmentación de gliomas en imagen de resonancia magnética multimodal: Glioma segmentation in multimodal magnetic resonance imaging. [Masters Thesis]. Universidad de Cantabria; 2019. Available from: http://hdl.handle.net/10902/17859


California State Polytechnic University – Pomona

4. 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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Shimpi S. Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients. [Internet] [Masters thesis]. California State Polytechnic University – Pomona; 2018. [cited 2021 Feb 27]. 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

5. 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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Windrim L. Illumination Invariant Deep Learning for Hyperspectral Data . [Internet] [Thesis]. University of Sydney; 2018. [cited 2021 Feb 27]. 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


University of Oxford

6. Lee, Namhoon. Toward efficient deep learning with sparse neural networks.

Degree: PhD, 2020, University of Oxford

 Despite the tremendous success that deep learning has achieved in recent years, it remains challenging to deal with the excessive computational and memory cost involved… (more)

Subjects/Keywords: Deep learning

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

APA (6th Edition):

Lee, N. (2020). Toward efficient deep learning with sparse neural networks. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:000e9d44-0229-48a3-84b0-dc17a8e96ccf ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820743

Chicago Manual of Style (16th Edition):

Lee, Namhoon. “Toward efficient deep learning with sparse neural networks.” 2020. Doctoral Dissertation, University of Oxford. Accessed February 27, 2021. http://ora.ox.ac.uk/objects/uuid:000e9d44-0229-48a3-84b0-dc17a8e96ccf ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820743.

MLA Handbook (7th Edition):

Lee, Namhoon. “Toward efficient deep learning with sparse neural networks.” 2020. Web. 27 Feb 2021.

Vancouver:

Lee N. Toward efficient deep learning with sparse neural networks. [Internet] [Doctoral dissertation]. University of Oxford; 2020. [cited 2021 Feb 27]. Available from: http://ora.ox.ac.uk/objects/uuid:000e9d44-0229-48a3-84b0-dc17a8e96ccf ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820743.

Council of Science Editors:

Lee N. Toward efficient deep learning with sparse neural networks. [Doctoral Dissertation]. University of Oxford; 2020. Available from: http://ora.ox.ac.uk/objects/uuid:000e9d44-0229-48a3-84b0-dc17a8e96ccf ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820743


Penn State University

7. Gupta, Samarth. Supervised Machine Learning for Region Assignment of Zebrafish Brain Nuclei based on Computational Assessment of Cell Neighborhoods.

Degree: 2020, Penn State University

 Histological studies provide cellular insights into tissue architecture and have been central to phenotyping and biological discovery. Synchrotron X-ray micro tomography of tissue, or “X-ray… (more)

Subjects/Keywords: Deep Learning; Microtomography

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APA (6th Edition):

Gupta, S. (2020). Supervised Machine Learning for Region Assignment of Zebrafish Brain Nuclei based on Computational Assessment of Cell Neighborhoods. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/17771sxg646

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):

Gupta, Samarth. “Supervised Machine Learning for Region Assignment of Zebrafish Brain Nuclei based on Computational Assessment of Cell Neighborhoods.” 2020. Thesis, Penn State University. Accessed February 27, 2021. https://submit-etda.libraries.psu.edu/catalog/17771sxg646.

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

MLA Handbook (7th Edition):

Gupta, Samarth. “Supervised Machine Learning for Region Assignment of Zebrafish Brain Nuclei based on Computational Assessment of Cell Neighborhoods.” 2020. Web. 27 Feb 2021.

Vancouver:

Gupta S. Supervised Machine Learning for Region Assignment of Zebrafish Brain Nuclei based on Computational Assessment of Cell Neighborhoods. [Internet] [Thesis]. Penn State University; 2020. [cited 2021 Feb 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/17771sxg646.

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

Council of Science Editors:

Gupta S. Supervised Machine Learning for Region Assignment of Zebrafish Brain Nuclei based on Computational Assessment of Cell Neighborhoods. [Thesis]. Penn State University; 2020. Available from: https://submit-etda.libraries.psu.edu/catalog/17771sxg646

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


Texas A&M University

8. Vallamkonda, Abhilash Rajendra Babu. Model Attack on Convolutional Neural Networks.

Degree: MS, Computer Science, 2019, Texas A&M University

Deep learning is a machine learning technique that enables computers to learn directly from images, text, or sound in the same way that people do.… (more)

Subjects/Keywords: Deep Learning; Security

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APA (6th Edition):

Vallamkonda, A. R. B. (2019). Model Attack on Convolutional Neural Networks. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/188808

Chicago Manual of Style (16th Edition):

Vallamkonda, Abhilash Rajendra Babu. “Model Attack on Convolutional Neural Networks.” 2019. Masters Thesis, Texas A&M University. Accessed February 27, 2021. http://hdl.handle.net/1969.1/188808.

MLA Handbook (7th Edition):

Vallamkonda, Abhilash Rajendra Babu. “Model Attack on Convolutional Neural Networks.” 2019. Web. 27 Feb 2021.

Vancouver:

Vallamkonda ARB. Model Attack on Convolutional Neural Networks. [Internet] [Masters thesis]. Texas A&M University; 2019. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1969.1/188808.

Council of Science Editors:

Vallamkonda ARB. Model Attack on Convolutional Neural Networks. [Masters Thesis]. Texas A&M University; 2019. Available from: http://hdl.handle.net/1969.1/188808


Georgia Tech

9. 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 February 27, 2021. http://hdl.handle.net/1853/60226.

MLA Handbook (7th Edition):

Choi, Edward. “Doctor AI: Interpretable deep learning for modeling electronic health records.” 2018. Web. 27 Feb 2021.

Vancouver:

Choi E. Doctor AI: Interpretable deep learning for modeling electronic health records. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Feb 27]. 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


Cornell University

10. Lenz, Ian. Deep Learning For Robotics.

Degree: PhD, Computer Science, 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. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/44317

Chicago Manual of Style (16th Edition):

Lenz, Ian. “Deep Learning For Robotics.” 2016. Doctoral Dissertation, Cornell University. Accessed February 27, 2021. http://hdl.handle.net/1813/44317.

MLA Handbook (7th Edition):

Lenz, Ian. “Deep Learning For Robotics.” 2016. Web. 27 Feb 2021.

Vancouver:

Lenz I. Deep Learning For Robotics. [Internet] [Doctoral dissertation]. Cornell University; 2016. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1813/44317.

Council of Science Editors:

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


University of KwaZulu-Natal

11. 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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Govender L. Determination of quantum entanglement concurrence using multilayer perceptron neural networks. [Internet] [Thesis]. University of KwaZulu-Natal; 2017. [cited 2021 Feb 27]. 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


Princeton University

12. Ravi, Sachin. Meta-Learning for Data and Processing Efficiency .

Degree: PhD, 2019, Princeton University

Deep learning models have shown great success in a variety of machine learning benchmarks; however, these models still lack the efficiency and flexibility of humans.… (more)

Subjects/Keywords: Deep Learning; Meta-Learning

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APA (6th Edition):

Ravi, S. (2019). Meta-Learning for Data and Processing Efficiency . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp013j333513x

Chicago Manual of Style (16th Edition):

Ravi, Sachin. “Meta-Learning for Data and Processing Efficiency .” 2019. Doctoral Dissertation, Princeton University. Accessed February 27, 2021. http://arks.princeton.edu/ark:/88435/dsp013j333513x.

MLA Handbook (7th Edition):

Ravi, Sachin. “Meta-Learning for Data and Processing Efficiency .” 2019. Web. 27 Feb 2021.

Vancouver:

Ravi S. Meta-Learning for Data and Processing Efficiency . [Internet] [Doctoral dissertation]. Princeton University; 2019. [cited 2021 Feb 27]. Available from: http://arks.princeton.edu/ark:/88435/dsp013j333513x.

Council of Science Editors:

Ravi S. Meta-Learning for Data and Processing Efficiency . [Doctoral Dissertation]. Princeton University; 2019. Available from: http://arks.princeton.edu/ark:/88435/dsp013j333513x


Princeton University

13. Ravi, Sachin. Meta-Learning for Data and Processing Efficiency .

Degree: PhD, 2019, Princeton University

Deep learning models have shown great success in a variety of machine learning benchmarks; however, these models still lack the efficiency and flexibility of humans.… (more)

Subjects/Keywords: Deep Learning; Meta-Learning

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

APA (6th Edition):

Ravi, S. (2019). Meta-Learning for Data and Processing Efficiency . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01ns064891r

Chicago Manual of Style (16th Edition):

Ravi, Sachin. “Meta-Learning for Data and Processing Efficiency .” 2019. Doctoral Dissertation, Princeton University. Accessed February 27, 2021. http://arks.princeton.edu/ark:/88435/dsp01ns064891r.

MLA Handbook (7th Edition):

Ravi, Sachin. “Meta-Learning for Data and Processing Efficiency .” 2019. Web. 27 Feb 2021.

Vancouver:

Ravi S. Meta-Learning for Data and Processing Efficiency . [Internet] [Doctoral dissertation]. Princeton University; 2019. [cited 2021 Feb 27]. Available from: http://arks.princeton.edu/ark:/88435/dsp01ns064891r.

Council of Science Editors:

Ravi S. Meta-Learning for Data and Processing Efficiency . [Doctoral Dissertation]. Princeton University; 2019. Available from: http://arks.princeton.edu/ark:/88435/dsp01ns064891r


University of Illinois – Urbana-Champaign

14. 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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Deshpande I. Generative modeling using the sliced Wasserstein distance. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2021 Feb 27]. 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


University of Illinois – Urbana-Champaign

15. Liu, Jialin. Machine learning workflow optimization via automatic discovery of resource reuse opportunities.

Degree: MS, Computer Science, 2019, University of Illinois – Urbana-Champaign

 Many state-of-the-art deep learning models rely on dynamic computation logic, making them difficult to optimize. In this thesis, we present a hashing based algorithm that… (more)

Subjects/Keywords: Machine Learning; Deep Learning; System

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APA (6th Edition):

Liu, J. (2019). Machine learning workflow optimization via automatic discovery of resource reuse opportunities. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/104894

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

Chicago Manual of Style (16th Edition):

Liu, Jialin. “Machine learning workflow optimization via automatic discovery of resource reuse opportunities.” 2019. Thesis, University of Illinois – Urbana-Champaign. Accessed February 27, 2021. http://hdl.handle.net/2142/104894.

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

MLA Handbook (7th Edition):

Liu, Jialin. “Machine learning workflow optimization via automatic discovery of resource reuse opportunities.” 2019. Web. 27 Feb 2021.

Vancouver:

Liu J. Machine learning workflow optimization via automatic discovery of resource reuse opportunities. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2019. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2142/104894.

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

Council of Science Editors:

Liu J. Machine learning workflow optimization via automatic discovery of resource reuse opportunities. [Thesis]. University of Illinois – Urbana-Champaign; 2019. Available from: http://hdl.handle.net/2142/104894

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


California State University – Sacramento

16. 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 February 27, 2021. http://hdl.handle.net/10211.3/207660.

MLA Handbook (7th Edition):

Poosarla, Akshay. “Bone age prediction with convolutional neural networks.” 2019. Web. 27 Feb 2021.

Vancouver:

Poosarla A. Bone age prediction with convolutional neural networks. [Internet] [Masters thesis]. California State University – Sacramento; 2019. [cited 2021 Feb 27]. 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


University of Oxford

17. Berrada, Leonard. Leveraging structure for optimization in deep learning.

Degree: PhD, 2019, University of Oxford

 In the past decade, neural networks have demonstrated impressive performance in supervised learning. They now power many applications ranging from real- time medical diagnosis to… (more)

Subjects/Keywords: optimization; machine learning; deep learning

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APA (6th Edition):

Berrada, L. (2019). Leveraging structure for optimization in deep learning. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:79360a95-a6e0-4acc-ba3a-07598f52ea39 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820693

Chicago Manual of Style (16th Edition):

Berrada, Leonard. “Leveraging structure for optimization in deep learning.” 2019. Doctoral Dissertation, University of Oxford. Accessed February 27, 2021. http://ora.ox.ac.uk/objects/uuid:79360a95-a6e0-4acc-ba3a-07598f52ea39 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820693.

MLA Handbook (7th Edition):

Berrada, Leonard. “Leveraging structure for optimization in deep learning.” 2019. Web. 27 Feb 2021.

Vancouver:

Berrada L. Leveraging structure for optimization in deep learning. [Internet] [Doctoral dissertation]. University of Oxford; 2019. [cited 2021 Feb 27]. Available from: http://ora.ox.ac.uk/objects/uuid:79360a95-a6e0-4acc-ba3a-07598f52ea39 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820693.

Council of Science Editors:

Berrada L. Leveraging structure for optimization in deep learning. [Doctoral Dissertation]. University of Oxford; 2019. Available from: http://ora.ox.ac.uk/objects/uuid:79360a95-a6e0-4acc-ba3a-07598f52ea39 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820693


Australian National University

18. 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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Dong C. Spatial Deep Networks for Outdoor Scene Classification . [Internet] [Thesis]. Australian National University; 2015. [cited 2021 Feb 27]. 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

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

Degree: PhD, Computer science, 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Hausknecht, Matthew John. “Cooperation and communication in multiagent deep reinforcement learning.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed February 27, 2021. http://hdl.handle.net/2152/45681.

MLA Handbook (7th Edition):

Hausknecht, Matthew John. “Cooperation and communication in multiagent deep reinforcement learning.” 2016. Web. 27 Feb 2021.

Vancouver:

Hausknecht MJ. Cooperation and communication in multiagent deep reinforcement learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2152/45681.

Council of Science Editors:

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


University of Waterloo

20. Gaurav, Ashish. Safety-Oriented Stability Biases for Continual Learning.

Degree: 2020, University of Waterloo

 Continual learning is often confounded by “catastrophic forgetting” that prevents neural networks from learning tasks sequentially. In the case of real world classification systems that… (more)

Subjects/Keywords: deep learning; continual learning; classification; reinforcement learning

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APA (6th Edition):

Gaurav, A. (2020). Safety-Oriented Stability Biases for Continual Learning. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15579

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):

Gaurav, Ashish. “Safety-Oriented Stability Biases for Continual Learning.” 2020. Thesis, University of Waterloo. Accessed February 27, 2021. http://hdl.handle.net/10012/15579.

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

MLA Handbook (7th Edition):

Gaurav, Ashish. “Safety-Oriented Stability Biases for Continual Learning.” 2020. Web. 27 Feb 2021.

Vancouver:

Gaurav A. Safety-Oriented Stability Biases for Continual Learning. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10012/15579.

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

Council of Science Editors:

Gaurav A. Safety-Oriented Stability Biases for Continual Learning. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/15579

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


University of Guelph

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

Degree: Master of Applied Science, School of Engineering, 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. (Masters Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809

Chicago Manual of Style (16th Edition):

Im, Jiwoong. “Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems.” 2015. Masters Thesis, University of Guelph. Accessed February 27, 2021. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809.

MLA Handbook (7th Edition):

Im, Jiwoong. “Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems.” 2015. Web. 27 Feb 2021.

Vancouver:

Im J. Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems. [Internet] [Masters thesis]. University of Guelph; 2015. [cited 2021 Feb 27]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809.

Council of Science Editors:

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


University of Toronto

22. Makhzani, Alireza. Unsupervised Representation Learning with Autoencoders.

Degree: PhD, 2018, University of Toronto

 Despite the recent progress in machine learning and deep learning, unsupervised learning still remains a largely unsolved problem. It is widely recognized that unsupervised learning(more)

Subjects/Keywords: Deep Learning; Machine Learning; Unsupervised Learning; 0984

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APA (6th Edition):

Makhzani, A. (2018). Unsupervised Representation Learning with Autoencoders. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/89800

Chicago Manual of Style (16th Edition):

Makhzani, Alireza. “Unsupervised Representation Learning with Autoencoders.” 2018. Doctoral Dissertation, University of Toronto. Accessed February 27, 2021. http://hdl.handle.net/1807/89800.

MLA Handbook (7th Edition):

Makhzani, Alireza. “Unsupervised Representation Learning with Autoencoders.” 2018. Web. 27 Feb 2021.

Vancouver:

Makhzani A. Unsupervised Representation Learning with Autoencoders. [Internet] [Doctoral dissertation]. University of Toronto; 2018. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1807/89800.

Council of Science Editors:

Makhzani A. Unsupervised Representation Learning with Autoencoders. [Doctoral Dissertation]. University of Toronto; 2018. Available from: http://hdl.handle.net/1807/89800


University of Illinois – Urbana-Champaign

23. Benson, Christopher Edward. Improving cache replacement policy using deep reinforcement learning.

Degree: MS, Computer Science, 2018, University of Illinois – Urbana-Champaign

 This thesis explores the use of reinforcement learning approaches to improve replacement policies of caches. In today's internet, caches play a vital role in improving… (more)

Subjects/Keywords: Reinforcement Learning; Machine Learning; Deep Learning

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APA (6th Edition):

Benson, C. E. (2018). Improving cache replacement policy using deep reinforcement learning. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/102858

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):

Benson, Christopher Edward. “Improving cache replacement policy using deep reinforcement learning.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed February 27, 2021. http://hdl.handle.net/2142/102858.

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

MLA Handbook (7th Edition):

Benson, Christopher Edward. “Improving cache replacement policy using deep reinforcement learning.” 2018. Web. 27 Feb 2021.

Vancouver:

Benson CE. Improving cache replacement policy using deep reinforcement learning. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2142/102858.

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

Council of Science Editors:

Benson CE. Improving cache replacement policy using deep reinforcement learning. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/102858

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


NSYSU

24. 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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Lin K. Deep Reinforcement Learning with a Gating Network. [Internet] [Thesis]. NSYSU; 2017. [cited 2021 Feb 27]. 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


Rochester Institute of Technology

25. Lamos-Sweeney, Joshua. Deep learning using genetic algorithms.

Degree: Computer Science (GCCIS), 2012, Rochester Institute of Technology

Deep Learning networks are a new type of neural network that discovers important object features. These networks determine features without supervision, and are adept at… (more)

Subjects/Keywords: Deep learning; Genetic algorithms

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APA (6th Edition):

Lamos-Sweeney, J. (2012). Deep learning using genetic algorithms. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/254

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):

Lamos-Sweeney, Joshua. “Deep learning using genetic algorithms.” 2012. Thesis, Rochester Institute of Technology. Accessed February 27, 2021. https://scholarworks.rit.edu/theses/254.

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

MLA Handbook (7th Edition):

Lamos-Sweeney, Joshua. “Deep learning using genetic algorithms.” 2012. Web. 27 Feb 2021.

Vancouver:

Lamos-Sweeney J. Deep learning using genetic algorithms. [Internet] [Thesis]. Rochester Institute of Technology; 2012. [cited 2021 Feb 27]. Available from: https://scholarworks.rit.edu/theses/254.

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

Council of Science Editors:

Lamos-Sweeney J. Deep learning using genetic algorithms. [Thesis]. Rochester Institute of Technology; 2012. Available from: https://scholarworks.rit.edu/theses/254

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


Rochester Institute of Technology

26. Makhija, Sidharth. Graph Networks for Multi-Label Image Recognition.

Degree: MS, Computer Engineering, 2020, Rochester Institute of Technology

  Providing machines with a robust visualization of multiple objects in a scene has a myriad of applications in the physical world. This research solves… (more)

Subjects/Keywords: Convolution; Deep learning; Graph network

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APA (6th Edition):

Makhija, S. (2020). Graph Networks for Multi-Label Image Recognition. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10541

Chicago Manual of Style (16th Edition):

Makhija, Sidharth. “Graph Networks for Multi-Label Image Recognition.” 2020. Masters Thesis, Rochester Institute of Technology. Accessed February 27, 2021. https://scholarworks.rit.edu/theses/10541.

MLA Handbook (7th Edition):

Makhija, Sidharth. “Graph Networks for Multi-Label Image Recognition.” 2020. Web. 27 Feb 2021.

Vancouver:

Makhija S. Graph Networks for Multi-Label Image Recognition. [Internet] [Masters thesis]. Rochester Institute of Technology; 2020. [cited 2021 Feb 27]. Available from: https://scholarworks.rit.edu/theses/10541.

Council of Science Editors:

Makhija S. Graph Networks for Multi-Label Image Recognition. [Masters Thesis]. Rochester Institute of Technology; 2020. Available from: https://scholarworks.rit.edu/theses/10541


San Jose State University

27. Gopalakrishnan Elango, Sindhuja. Convolutional Neural Network Acceleration on GPU by Exploiting Data Reuse.

Degree: MS, Computer Engineering, 2017, San Jose State University

  Graphical processing units (GPUs) achieve high throughput with hundreds of cores for concurrent execution and a large register file for storing the context of… (more)

Subjects/Keywords: Deep Learning; Energy-efficiency; GPU

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APA (6th Edition):

Gopalakrishnan Elango, S. (2017). Convolutional Neural Network Acceleration on GPU by Exploiting Data Reuse. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.9b4r-na7x ; https://scholarworks.sjsu.edu/etd_theses/4800

Chicago Manual of Style (16th Edition):

Gopalakrishnan Elango, Sindhuja. “Convolutional Neural Network Acceleration on GPU by Exploiting Data Reuse.” 2017. Masters Thesis, San Jose State University. Accessed February 27, 2021. https://doi.org/10.31979/etd.9b4r-na7x ; https://scholarworks.sjsu.edu/etd_theses/4800.

MLA Handbook (7th Edition):

Gopalakrishnan Elango, Sindhuja. “Convolutional Neural Network Acceleration on GPU by Exploiting Data Reuse.” 2017. Web. 27 Feb 2021.

Vancouver:

Gopalakrishnan Elango S. Convolutional Neural Network Acceleration on GPU by Exploiting Data Reuse. [Internet] [Masters thesis]. San Jose State University; 2017. [cited 2021 Feb 27]. Available from: https://doi.org/10.31979/etd.9b4r-na7x ; https://scholarworks.sjsu.edu/etd_theses/4800.

Council of Science Editors:

Gopalakrishnan Elango S. Convolutional Neural Network Acceleration on GPU by Exploiting Data Reuse. [Masters Thesis]. San Jose State University; 2017. Available from: https://doi.org/10.31979/etd.9b4r-na7x ; https://scholarworks.sjsu.edu/etd_theses/4800


Rochester Institute of Technology

28. Petroski Such, Felipe. Deep Learning Architectures for Novel Problems.

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

  With convolutional neural networks revolutionizing the computer vision field it is important to extend the capabilities of neural-based systems to dynamic and unrestricted data… (more)

Subjects/Keywords: Deep learning; ICR; Machine intelligence

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APA (6th Edition):

Petroski Such, F. (2017). Deep Learning Architectures for Novel Problems. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9611

Chicago Manual of Style (16th Edition):

Petroski Such, Felipe. “Deep Learning Architectures for Novel Problems.” 2017. Masters Thesis, Rochester Institute of Technology. Accessed February 27, 2021. https://scholarworks.rit.edu/theses/9611.

MLA Handbook (7th Edition):

Petroski Such, Felipe. “Deep Learning Architectures for Novel Problems.” 2017. Web. 27 Feb 2021.

Vancouver:

Petroski Such F. Deep Learning Architectures for Novel Problems. [Internet] [Masters thesis]. Rochester Institute of Technology; 2017. [cited 2021 Feb 27]. Available from: https://scholarworks.rit.edu/theses/9611.

Council of Science Editors:

Petroski Such F. Deep Learning Architectures for Novel Problems. [Masters Thesis]. Rochester Institute of Technology; 2017. Available from: https://scholarworks.rit.edu/theses/9611


McMaster University

29. 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 February 27, 2021. http://hdl.handle.net/11375/24290.

MLA Handbook (7th Edition):

Chi, Zhixiang. “IMAGE RESTORATIONS USING DEEP LEARNING TECHNIQUES.” 2018. Web. 27 Feb 2021.

Vancouver:

Chi Z. IMAGE RESTORATIONS USING DEEP LEARNING TECHNIQUES. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2021 Feb 27]. 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


Penn State University

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

Degree: 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. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/12477njl5114

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):

Lageman, Nathaniel John. “BinDNN: Resilient Function Matching Using Deep Learning.” 2016. Thesis, Penn State University. Accessed February 27, 2021. https://submit-etda.libraries.psu.edu/catalog/12477njl5114.

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

MLA Handbook (7th Edition):

Lageman, Nathaniel John. “BinDNN: Resilient Function Matching Using Deep Learning.” 2016. Web. 27 Feb 2021.

Vancouver:

Lageman NJ. BinDNN: Resilient Function Matching Using Deep Learning. [Internet] [Thesis]. Penn State University; 2016. [cited 2021 Feb 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/12477njl5114.

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

Council of Science Editors:

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

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

[1] [2] [3] [4] [5] … [103]

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