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You searched for subject:(generative model). Showing records 1 – 30 of 50 total matches.

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University of Illinois – Urbana-Champaign

1. Chan, Po-Wei. Probabilistic interpretation of path-based relevance in heterogeneous information networks.

Degree: MS, Computer Science, 2017, University of Illinois – Urbana-Champaign

 As a powerful representation paradigm for networked and multi-typed data, the heterogeneous information network (HIN) is ubiquitous. Meanwhile, defining proper relevance measures has always been… (more)

Subjects/Keywords: Relevance measure; Heterogeneous information network; Generative model

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

APA (6th Edition):

Chan, P. (2017). Probabilistic interpretation of path-based relevance in heterogeneous information networks. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/97416

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):

Chan, Po-Wei. “Probabilistic interpretation of path-based relevance in heterogeneous information networks.” 2017. Thesis, University of Illinois – Urbana-Champaign. Accessed November 19, 2019. http://hdl.handle.net/2142/97416.

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

MLA Handbook (7th Edition):

Chan, Po-Wei. “Probabilistic interpretation of path-based relevance in heterogeneous information networks.” 2017. Web. 19 Nov 2019.

Vancouver:

Chan P. Probabilistic interpretation of path-based relevance in heterogeneous information networks. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2017. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/2142/97416.

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

Council of Science Editors:

Chan P. Probabilistic interpretation of path-based relevance in heterogeneous information networks. [Thesis]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/97416

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


Clemson University

2. Guo, Hanyu. One-shot Learning In Deep Sequential Generative Models.

Degree: MS, Electrical and Computer Engineering (Holcomb Dept. of), 2017, Clemson University

 Regardless of the Deep Learning community's continuous advancements, the challenging domain of one-shot learning still persists. While the human brain is capable of learning a… (more)

Subjects/Keywords: generative model; meta learning; metric learning; one-shot learning; sequential model

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APA (6th Edition):

Guo, H. (2017). One-shot Learning In Deep Sequential Generative Models. (Masters Thesis). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_theses/2792

Chicago Manual of Style (16th Edition):

Guo, Hanyu. “One-shot Learning In Deep Sequential Generative Models.” 2017. Masters Thesis, Clemson University. Accessed November 19, 2019. https://tigerprints.clemson.edu/all_theses/2792.

MLA Handbook (7th Edition):

Guo, Hanyu. “One-shot Learning In Deep Sequential Generative Models.” 2017. Web. 19 Nov 2019.

Vancouver:

Guo H. One-shot Learning In Deep Sequential Generative Models. [Internet] [Masters thesis]. Clemson University; 2017. [cited 2019 Nov 19]. Available from: https://tigerprints.clemson.edu/all_theses/2792.

Council of Science Editors:

Guo H. One-shot Learning In Deep Sequential Generative Models. [Masters Thesis]. Clemson University; 2017. Available from: https://tigerprints.clemson.edu/all_theses/2792


UCLA

3. Han, Tian. Unsupervised Learning and Understanding of Deep Generative Models.

Degree: Statistics, 2019, UCLA

 Probabilistic generative models, especially ones that are parametrized by convolutional neural network (ConvNet), are compact representation tools towards knowledge understanding and can be crucial in… (more)

Subjects/Keywords: Statistics; computer vision; energy based model; generative model; sampling; unsupervised learning

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APA (6th Edition):

Han, T. (2019). Unsupervised Learning and Understanding of Deep Generative Models. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/1tx2496r

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):

Han, Tian. “Unsupervised Learning and Understanding of Deep Generative Models.” 2019. Thesis, UCLA. Accessed November 19, 2019. http://www.escholarship.org/uc/item/1tx2496r.

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

MLA Handbook (7th Edition):

Han, Tian. “Unsupervised Learning and Understanding of Deep Generative Models.” 2019. Web. 19 Nov 2019.

Vancouver:

Han T. Unsupervised Learning and Understanding of Deep Generative Models. [Internet] [Thesis]. UCLA; 2019. [cited 2019 Nov 19]. Available from: http://www.escholarship.org/uc/item/1tx2496r.

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

Council of Science Editors:

Han T. Unsupervised Learning and Understanding of Deep Generative Models. [Thesis]. UCLA; 2019. Available from: http://www.escholarship.org/uc/item/1tx2496r

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


Oklahoma State University

4. Ding, Yi. Probabilistic Graphic Models for Sports Video Mining: Hybrid Generative-discriminative Approaches.

Degree: School of Electrical & Computer Engineering, 2010, Oklahoma State University

 With the development of multimedia and internet technologies, there is a growing interest in video mining research that is to discover knowledge existing in the… (more)

Subjects/Keywords: discriminative; generative; graphical model; machine learning; sports video; video mining

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APA (6th Edition):

Ding, Y. (2010). Probabilistic Graphic Models for Sports Video Mining: Hybrid Generative-discriminative Approaches. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/7854

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):

Ding, Yi. “Probabilistic Graphic Models for Sports Video Mining: Hybrid Generative-discriminative Approaches.” 2010. Thesis, Oklahoma State University. Accessed November 19, 2019. http://hdl.handle.net/11244/7854.

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

MLA Handbook (7th Edition):

Ding, Yi. “Probabilistic Graphic Models for Sports Video Mining: Hybrid Generative-discriminative Approaches.” 2010. Web. 19 Nov 2019.

Vancouver:

Ding Y. Probabilistic Graphic Models for Sports Video Mining: Hybrid Generative-discriminative Approaches. [Internet] [Thesis]. Oklahoma State University; 2010. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/11244/7854.

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

Council of Science Editors:

Ding Y. Probabilistic Graphic Models for Sports Video Mining: Hybrid Generative-discriminative Approaches. [Thesis]. Oklahoma State University; 2010. Available from: http://hdl.handle.net/11244/7854

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


University of Illinois – Urbana-Champaign

5. Zhang, Yang. Probabilistic generative modeling of speech.

Degree: MS, Electrical & Computer Engineering, 2015, University of Illinois – Urbana-Champaign

 Speech processing refers to a set of tasks that involve speech analysis and synthesis. Most speech processing algorithms model a subset of speech parameters of… (more)

Subjects/Keywords: Probabilistic acoustic tube; speech modeling; speech analysis; generative model

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

APA (6th Edition):

Zhang, Y. (2015). Probabilistic generative modeling of speech. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/89006

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

Chicago Manual of Style (16th Edition):

Zhang, Yang. “Probabilistic generative modeling of speech.” 2015. Thesis, University of Illinois – Urbana-Champaign. Accessed November 19, 2019. http://hdl.handle.net/2142/89006.

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

MLA Handbook (7th Edition):

Zhang, Yang. “Probabilistic generative modeling of speech.” 2015. Web. 19 Nov 2019.

Vancouver:

Zhang Y. Probabilistic generative modeling of speech. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/2142/89006.

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

Council of Science Editors:

Zhang Y. Probabilistic generative modeling of speech. [Thesis]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/89006

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


Rice University

6. Nguyen, Minh Tan. The Deep Rendering Model: Bridging Theory and Practice in Deep Learning.

Degree: MS, Engineering, 2018, Rice University

 A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks such as visual object… (more)

Subjects/Keywords: deep learning; deep convolutional network; generative model; semi-supervised learning

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APA (6th Edition):

Nguyen, M. T. (2018). The Deep Rendering Model: Bridging Theory and Practice in Deep Learning. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/105801

Chicago Manual of Style (16th Edition):

Nguyen, Minh Tan. “The Deep Rendering Model: Bridging Theory and Practice in Deep Learning.” 2018. Masters Thesis, Rice University. Accessed November 19, 2019. http://hdl.handle.net/1911/105801.

MLA Handbook (7th Edition):

Nguyen, Minh Tan. “The Deep Rendering Model: Bridging Theory and Practice in Deep Learning.” 2018. Web. 19 Nov 2019.

Vancouver:

Nguyen MT. The Deep Rendering Model: Bridging Theory and Practice in Deep Learning. [Internet] [Masters thesis]. Rice University; 2018. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/1911/105801.

Council of Science Editors:

Nguyen MT. The Deep Rendering Model: Bridging Theory and Practice in Deep Learning. [Masters Thesis]. Rice University; 2018. Available from: http://hdl.handle.net/1911/105801


University of New South Wales

7. Lim, Hock Chuan. Interplay of ethical trust and social moral norms in signal-behaviour computational social processes : an investigation of agents and networks effects.

Degree: Electrical Engineering, 2011, University of New South Wales

 Computational modelling and simulation in social science research advocate that social science theories should guide and inform model formulations and the models in turn aid… (more)

Subjects/Keywords: Ethical disposition; Agent-based model; Ethical trust; Generative social simulation; Generative experiment; Moral norms; Normative social systems

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APA (6th Edition):

Lim, H. C. (2011). Interplay of ethical trust and social moral norms in signal-behaviour computational social processes : an investigation of agents and networks effects. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/51447 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10133/SOURCE01?view=true

Chicago Manual of Style (16th Edition):

Lim, Hock Chuan. “Interplay of ethical trust and social moral norms in signal-behaviour computational social processes : an investigation of agents and networks effects.” 2011. Doctoral Dissertation, University of New South Wales. Accessed November 19, 2019. http://handle.unsw.edu.au/1959.4/51447 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10133/SOURCE01?view=true.

MLA Handbook (7th Edition):

Lim, Hock Chuan. “Interplay of ethical trust and social moral norms in signal-behaviour computational social processes : an investigation of agents and networks effects.” 2011. Web. 19 Nov 2019.

Vancouver:

Lim HC. Interplay of ethical trust and social moral norms in signal-behaviour computational social processes : an investigation of agents and networks effects. [Internet] [Doctoral dissertation]. University of New South Wales; 2011. [cited 2019 Nov 19]. Available from: http://handle.unsw.edu.au/1959.4/51447 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10133/SOURCE01?view=true.

Council of Science Editors:

Lim HC. Interplay of ethical trust and social moral norms in signal-behaviour computational social processes : an investigation of agents and networks effects. [Doctoral Dissertation]. University of New South Wales; 2011. Available from: http://handle.unsw.edu.au/1959.4/51447 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10133/SOURCE01?view=true


University of Edinburgh

8. Srivastava, Akash. Deep generative modelling for amortised variational inference.

Degree: PhD, 2019, University of Edinburgh

 Probabilistic and statistical modelling are the fundamental frameworks that underlie a large proportion of the modern machine learning (ML) techniques. These frameworks allow for the… (more)

Subjects/Keywords: model-specific inference; Bayesian generative models; VEEGAN; discriminator based density ratio estimator; generative modelling; variational inference

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APA (6th Edition):

Srivastava, A. (2019). Deep generative modelling for amortised variational inference. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/36114

Chicago Manual of Style (16th Edition):

Srivastava, Akash. “Deep generative modelling for amortised variational inference.” 2019. Doctoral Dissertation, University of Edinburgh. Accessed November 19, 2019. http://hdl.handle.net/1842/36114.

MLA Handbook (7th Edition):

Srivastava, Akash. “Deep generative modelling for amortised variational inference.” 2019. Web. 19 Nov 2019.

Vancouver:

Srivastava A. Deep generative modelling for amortised variational inference. [Internet] [Doctoral dissertation]. University of Edinburgh; 2019. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/1842/36114.

Council of Science Editors:

Srivastava A. Deep generative modelling for amortised variational inference. [Doctoral Dissertation]. University of Edinburgh; 2019. Available from: http://hdl.handle.net/1842/36114


University of Edinburgh

9. Moreno Comellas, Pol. Vision as inverse graphics for detailed scene understanding.

Degree: PhD, 2019, University of Edinburgh

 An image of a scene can be described by the shape, pose and appearance of the objects within it, as well as the illumination, and… (more)

Subjects/Keywords: generative models; recognition model; shadowing model; computer vision; vision as inverse graphics; inverse graphics

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APA (6th Edition):

Moreno Comellas, P. (2019). Vision as inverse graphics for detailed scene understanding. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/35541

Chicago Manual of Style (16th Edition):

Moreno Comellas, Pol. “Vision as inverse graphics for detailed scene understanding.” 2019. Doctoral Dissertation, University of Edinburgh. Accessed November 19, 2019. http://hdl.handle.net/1842/35541.

MLA Handbook (7th Edition):

Moreno Comellas, Pol. “Vision as inverse graphics for detailed scene understanding.” 2019. Web. 19 Nov 2019.

Vancouver:

Moreno Comellas P. Vision as inverse graphics for detailed scene understanding. [Internet] [Doctoral dissertation]. University of Edinburgh; 2019. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/1842/35541.

Council of Science Editors:

Moreno Comellas P. Vision as inverse graphics for detailed scene understanding. [Doctoral Dissertation]. University of Edinburgh; 2019. Available from: http://hdl.handle.net/1842/35541


KTH

10. Nilsson, Mårten. Augmenting High-Dimensional Data with Deep Generative Models.

Degree: RPL, 2018, KTH

Data augmentation is a technique that can be performed in various ways to improve the training of discriminative models. The recent developments in deep… (more)

Subjects/Keywords: GAN; GANs; machine learning; deep learning; generative model; generative models; deep generative model; deep generative models; generative adversarial networks; VAE; VAEs; variational autoencoder; variational autoencoders; autoencoder; auto encoder; encoder; decoder; computer vision; eye tracking; pupil localization; pupil; eyes; eye; synthetic data; big data; data generation; synthetic data generation; neural networks; neural network; high-dimensional data; high-resolution images.; Computer Sciences; Datavetenskap (datalogi)

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APA (6th Edition):

Nilsson, M. (2018). Augmenting High-Dimensional Data with Deep Generative Models. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969

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):

Nilsson, Mårten. “Augmenting High-Dimensional Data with Deep Generative Models.” 2018. Thesis, KTH. Accessed November 19, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969.

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

MLA Handbook (7th Edition):

Nilsson, Mårten. “Augmenting High-Dimensional Data with Deep Generative Models.” 2018. Web. 19 Nov 2019.

Vancouver:

Nilsson M. Augmenting High-Dimensional Data with Deep Generative Models. [Internet] [Thesis]. KTH; 2018. [cited 2019 Nov 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969.

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

Council of Science Editors:

Nilsson M. Augmenting High-Dimensional Data with Deep Generative Models. [Thesis]. KTH; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969

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


UCLA

11. Lu, Yang. Coupling and Learning Hierarchical Generative and Descriptive Models for Image Synthesis and Analysis.

Degree: Statistics, 2017, UCLA

 Learning a generative model with compositional structure is a fundamental problem in statistics. My thesis generalizes two major classical statistical models by introducing the Convolutional… (more)

Subjects/Keywords: Statistics; Computer science; ConvNet; Generative Learning; Langevin; Latent factor model; Markov Random Field

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APA (6th Edition):

Lu, Y. (2017). Coupling and Learning Hierarchical Generative and Descriptive Models for Image Synthesis and Analysis. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/45n9n45p

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):

Lu, Yang. “Coupling and Learning Hierarchical Generative and Descriptive Models for Image Synthesis and Analysis.” 2017. Thesis, UCLA. Accessed November 19, 2019. http://www.escholarship.org/uc/item/45n9n45p.

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

MLA Handbook (7th Edition):

Lu, Yang. “Coupling and Learning Hierarchical Generative and Descriptive Models for Image Synthesis and Analysis.” 2017. Web. 19 Nov 2019.

Vancouver:

Lu Y. Coupling and Learning Hierarchical Generative and Descriptive Models for Image Synthesis and Analysis. [Internet] [Thesis]. UCLA; 2017. [cited 2019 Nov 19]. Available from: http://www.escholarship.org/uc/item/45n9n45p.

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

Council of Science Editors:

Lu Y. Coupling and Learning Hierarchical Generative and Descriptive Models for Image Synthesis and Analysis. [Thesis]. UCLA; 2017. Available from: http://www.escholarship.org/uc/item/45n9n45p

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


University of California – San Diego

12. Zhang, Xinyu. A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA.

Degree: Computer Science, 2017, University of California – San Diego

 In recent years deep learning algorithms have shown extremely high performance on machine learning tasks such as image classification and speech recognition. In support of… (more)

Subjects/Keywords: Computer science; Computer engineering; Statistics; Acceleration; Deconvolution; FPGA; Generative Model; Neural Network

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APA (6th Edition):

Zhang, X. (2017). A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/01b3n7qb

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

Chicago Manual of Style (16th Edition):

Zhang, Xinyu. “A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA.” 2017. Thesis, University of California – San Diego. Accessed November 19, 2019. http://www.escholarship.org/uc/item/01b3n7qb.

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

MLA Handbook (7th Edition):

Zhang, Xinyu. “A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA.” 2017. Web. 19 Nov 2019.

Vancouver:

Zhang X. A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA. [Internet] [Thesis]. University of California – San Diego; 2017. [cited 2019 Nov 19]. Available from: http://www.escholarship.org/uc/item/01b3n7qb.

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

Council of Science Editors:

Zhang X. A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA. [Thesis]. University of California – San Diego; 2017. Available from: http://www.escholarship.org/uc/item/01b3n7qb

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


Duke University

13. Pu, Yunchen. Deep Generative Models for Image Representation Learning .

Degree: 2018, Duke University

  Recently there has been increasing interest in developing generative models of data, offering the promise of learning based on the often vast quantity of… (more)

Subjects/Keywords: Artificial intelligence; Canadian history; deep learning; generative model; image representation; machine learning; variational autoencoder

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APA (6th Edition):

Pu, Y. (2018). Deep Generative Models for Image Representation Learning . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/16806

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):

Pu, Yunchen. “Deep Generative Models for Image Representation Learning .” 2018. Thesis, Duke University. Accessed November 19, 2019. http://hdl.handle.net/10161/16806.

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

MLA Handbook (7th Edition):

Pu, Yunchen. “Deep Generative Models for Image Representation Learning .” 2018. Web. 19 Nov 2019.

Vancouver:

Pu Y. Deep Generative Models for Image Representation Learning . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/10161/16806.

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

Council of Science Editors:

Pu Y. Deep Generative Models for Image Representation Learning . [Thesis]. Duke University; 2018. Available from: http://hdl.handle.net/10161/16806

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


Brno University of Technology

14. Richtarik, Lukáš. Chatbot postavený na umělých neuronových sítích .

Degree: 2018, Brno University of Technology

 Táto práca sa zaoberá problematikou chatbotov postavených na umelých neurónových sieťach a generatívnych modeloch. Popisuje postup a možnosti pri návrhu takéhoto chatbota a taktiež samotnú… (more)

Subjects/Keywords: chatbot; neurónová sieť; BLEU; generatívny model; sequence to sequence; chatbot; neural network; BLEU; generative model; sequence to sequence

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APA (6th Edition):

Richtarik, L. (2018). Chatbot postavený na umělých neuronových sítích . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/85037

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):

Richtarik, Lukáš. “Chatbot postavený na umělých neuronových sítích .” 2018. Thesis, Brno University of Technology. Accessed November 19, 2019. http://hdl.handle.net/11012/85037.

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

MLA Handbook (7th Edition):

Richtarik, Lukáš. “Chatbot postavený na umělých neuronových sítích .” 2018. Web. 19 Nov 2019.

Vancouver:

Richtarik L. Chatbot postavený na umělých neuronových sítích . [Internet] [Thesis]. Brno University of Technology; 2018. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/11012/85037.

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

Council of Science Editors:

Richtarik L. Chatbot postavený na umělých neuronových sítích . [Thesis]. Brno University of Technology; 2018. Available from: http://hdl.handle.net/11012/85037

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


Brno University of Technology

15. Vaverka, Filip. Lokalizace mobilního robota pomocí kamery .

Degree: 2015, Brno University of Technology

 Tato práce popisuje návrh a realizaci metody lokalizace mobilního robota. Metoda je založena čistě na obrazových datech získaných pomocí monokulární kamery. Lokalizace je v popisovaném… (more)

Subjects/Keywords: vizuální lokalizace; monokulární kamera; topologický prostor; pravděpodobnostní model; generativní model; náhodné grafy; appearance-based localization; monocular camera; topological space; probabilistic model; generative model; random graphs

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

APA (6th Edition):

Vaverka, F. (2015). Lokalizace mobilního robota pomocí kamery . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/52309

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):

Vaverka, Filip. “Lokalizace mobilního robota pomocí kamery .” 2015. Thesis, Brno University of Technology. Accessed November 19, 2019. http://hdl.handle.net/11012/52309.

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

MLA Handbook (7th Edition):

Vaverka, Filip. “Lokalizace mobilního robota pomocí kamery .” 2015. Web. 19 Nov 2019.

Vancouver:

Vaverka F. Lokalizace mobilního robota pomocí kamery . [Internet] [Thesis]. Brno University of Technology; 2015. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/11012/52309.

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

Council of Science Editors:

Vaverka F. Lokalizace mobilního robota pomocí kamery . [Thesis]. Brno University of Technology; 2015. Available from: http://hdl.handle.net/11012/52309

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


Université de Montréal

16. Bordes, Florian. Learning to sample from noise with deep generative models .

Degree: 2017, Université de Montréal

 L’apprentissage automatique et spécialement l’apprentissage profond se sont imposés ces dernières années pour résoudre une large variété de tâches. Une des applications les plus remarquables… (more)

Subjects/Keywords: Apprentissage automatique; Apprentissage profond; Intelligence artificielle; Modèle Génératif; Infusion; Machine learning; Deep learning; Artificial Intelligence; Generative model

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

APA (6th Edition):

Bordes, F. (2017). Learning to sample from noise with deep generative models . (Thesis). Université de Montréal. Retrieved from http://hdl.handle.net/1866/19370

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):

Bordes, Florian. “Learning to sample from noise with deep generative models .” 2017. Thesis, Université de Montréal. Accessed November 19, 2019. http://hdl.handle.net/1866/19370.

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

MLA Handbook (7th Edition):

Bordes, Florian. “Learning to sample from noise with deep generative models .” 2017. Web. 19 Nov 2019.

Vancouver:

Bordes F. Learning to sample from noise with deep generative models . [Internet] [Thesis]. Université de Montréal; 2017. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/1866/19370.

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

Council of Science Editors:

Bordes F. Learning to sample from noise with deep generative models . [Thesis]. Université de Montréal; 2017. Available from: http://hdl.handle.net/1866/19370

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


The Ohio State University

17. Park, Kyoung Jin. Generating Thematic Maps from Hyperspectral Imagery Using a Bag-of-Materials Model.

Degree: PhD, Geodetic Science and Surveying, 2013, The Ohio State University

 Obtaining information about Earth’s surfaces and generating a land cover map is essential to remote sensing research. For that purpose, identifying and classifying the characteristics… (more)

Subjects/Keywords: Remote Sensing; Computer Engineering; Computer Science; Geographic Information Science; Hyperspectral image clustering; probabilistic topic modeling; generative model; latent dirichlet allocation

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

APA (6th Edition):

Park, K. J. (2013). Generating Thematic Maps from Hyperspectral Imagery Using a Bag-of-Materials Model. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1366296426

Chicago Manual of Style (16th Edition):

Park, Kyoung Jin. “Generating Thematic Maps from Hyperspectral Imagery Using a Bag-of-Materials Model.” 2013. Doctoral Dissertation, The Ohio State University. Accessed November 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366296426.

MLA Handbook (7th Edition):

Park, Kyoung Jin. “Generating Thematic Maps from Hyperspectral Imagery Using a Bag-of-Materials Model.” 2013. Web. 19 Nov 2019.

Vancouver:

Park KJ. Generating Thematic Maps from Hyperspectral Imagery Using a Bag-of-Materials Model. [Internet] [Doctoral dissertation]. The Ohio State University; 2013. [cited 2019 Nov 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1366296426.

Council of Science Editors:

Park KJ. Generating Thematic Maps from Hyperspectral Imagery Using a Bag-of-Materials Model. [Doctoral Dissertation]. The Ohio State University; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1366296426


University of Oxford

18. Miao, Yishu. Deep generative models for natural language processing.

Degree: PhD, 2017, University of Oxford

 Deep generative models are essential to Natural Language Processing (NLP) due to their outstanding ability to use unlabelled data, to incorporate abundant linguistic features, and… (more)

Subjects/Keywords: Dialogue System; Neural Variational Inference; Natural Language Processing; Deep Generative Models; Variational Autoencoder; Topic Model; Summarisation; Deep Learning

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

APA (6th Edition):

Miao, Y. (2017). Deep generative models for natural language processing. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:e4e1f1f9-e507-4754-a0ab-0246f1e1e258 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748900

Chicago Manual of Style (16th Edition):

Miao, Yishu. “Deep generative models for natural language processing.” 2017. Doctoral Dissertation, University of Oxford. Accessed November 19, 2019. http://ora.ox.ac.uk/objects/uuid:e4e1f1f9-e507-4754-a0ab-0246f1e1e258 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748900.

MLA Handbook (7th Edition):

Miao, Yishu. “Deep generative models for natural language processing.” 2017. Web. 19 Nov 2019.

Vancouver:

Miao Y. Deep generative models for natural language processing. [Internet] [Doctoral dissertation]. University of Oxford; 2017. [cited 2019 Nov 19]. Available from: http://ora.ox.ac.uk/objects/uuid:e4e1f1f9-e507-4754-a0ab-0246f1e1e258 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748900.

Council of Science Editors:

Miao Y. Deep generative models for natural language processing. [Doctoral Dissertation]. University of Oxford; 2017. Available from: http://ora.ox.ac.uk/objects/uuid:e4e1f1f9-e507-4754-a0ab-0246f1e1e258 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748900


University of California – San Diego

19. Atalla, Chad E. Modifying Social Dimensions of Human Faces with ModifAE.

Degree: Computer Science and Engineering, 2019, University of California – San Diego

 At first glance, humans extract social judgments from faces, including how trustworthy, attractive, and aggressive they look. These impressions have profound social, economic, and political… (more)

Subjects/Keywords: Computer science; Artificial intelligence; Cognitive Science; Deep Learning; Face Recognition; Generative Model; Machine Learning; Neural Networks

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

APA (6th Edition):

Atalla, C. E. (2019). Modifying Social Dimensions of Human Faces with ModifAE. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/7nw670qq

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):

Atalla, Chad E. “Modifying Social Dimensions of Human Faces with ModifAE.” 2019. Thesis, University of California – San Diego. Accessed November 19, 2019. http://www.escholarship.org/uc/item/7nw670qq.

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

MLA Handbook (7th Edition):

Atalla, Chad E. “Modifying Social Dimensions of Human Faces with ModifAE.” 2019. Web. 19 Nov 2019.

Vancouver:

Atalla CE. Modifying Social Dimensions of Human Faces with ModifAE. [Internet] [Thesis]. University of California – San Diego; 2019. [cited 2019 Nov 19]. Available from: http://www.escholarship.org/uc/item/7nw670qq.

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

Council of Science Editors:

Atalla CE. Modifying Social Dimensions of Human Faces with ModifAE. [Thesis]. University of California – San Diego; 2019. Available from: http://www.escholarship.org/uc/item/7nw670qq

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


KTH

20. Ionascu, Beatrice. Modelling user interaction at scale with deep generative methods.

Degree: Electrical Engineering and Computer Science (EECS), 2018, KTH

Understanding how users interact with a company's service is essential for data-driven businesses that want to better cater to their users and improve their… (more)

Subjects/Keywords: generative model; deep learning; variational auto-encoder; convolutional neural network; time-series; data reconstruction; Computer Sciences; Datavetenskap (datalogi)

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

APA (6th Edition):

Ionascu, B. (2018). Modelling user interaction at scale with deep generative methods. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239333

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):

Ionascu, Beatrice. “Modelling user interaction at scale with deep generative methods.” 2018. Thesis, KTH. Accessed November 19, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239333.

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

MLA Handbook (7th Edition):

Ionascu, Beatrice. “Modelling user interaction at scale with deep generative methods.” 2018. Web. 19 Nov 2019.

Vancouver:

Ionascu B. Modelling user interaction at scale with deep generative methods. [Internet] [Thesis]. KTH; 2018. [cited 2019 Nov 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239333.

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

Council of Science Editors:

Ionascu B. Modelling user interaction at scale with deep generative methods. [Thesis]. KTH; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239333

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


The Ohio State University

21. Yuan, Mengfei. Machine Learning-Based Reduced-Order Modeling and Uncertainty Quantification for "Structure-Property" Relations for ICME Applications.

Degree: PhD, Materials Science and Engineering, 2019, The Ohio State University

 The design framework for complex materials property and processing models within the Integrated Computational Material Engineering (ICME) is often hindered by the expensive computational cost.… (more)

Subjects/Keywords: Materials Science; Machine learning, Reduced-order model, ICME, Microstructure, Generative adversarial networks, Uncertainty quantification, Polynomial Chaos, Stochastic computations, Finite element analysis

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

APA (6th Edition):

Yuan, M. (2019). Machine Learning-Based Reduced-Order Modeling and Uncertainty Quantification for "Structure-Property" Relations for ICME Applications. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1555580083945861

Chicago Manual of Style (16th Edition):

Yuan, Mengfei. “Machine Learning-Based Reduced-Order Modeling and Uncertainty Quantification for "Structure-Property" Relations for ICME Applications.” 2019. Doctoral Dissertation, The Ohio State University. Accessed November 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555580083945861.

MLA Handbook (7th Edition):

Yuan, Mengfei. “Machine Learning-Based Reduced-Order Modeling and Uncertainty Quantification for "Structure-Property" Relations for ICME Applications.” 2019. Web. 19 Nov 2019.

Vancouver:

Yuan M. Machine Learning-Based Reduced-Order Modeling and Uncertainty Quantification for "Structure-Property" Relations for ICME Applications. [Internet] [Doctoral dissertation]. The Ohio State University; 2019. [cited 2019 Nov 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1555580083945861.

Council of Science Editors:

Yuan M. Machine Learning-Based Reduced-Order Modeling and Uncertainty Quantification for "Structure-Property" Relations for ICME Applications. [Doctoral Dissertation]. The Ohio State University; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1555580083945861


Massey University

22. Playne, Daniel Peter. Generative programming methods for parallel partial differential field equation solvers.

Degree: PhD, Computer Science, 2011, Massey University

 This thesis describes a generative programming system that automatically constructs parallel simulations of complex systems that are based on field equations using finite differencing and… (more)

Subjects/Keywords: Generative programming; Computer simulation; Parallel programming; Cahn-Hilliard model of phase separation; Ginzburg-Landau model of superconductivity; Lotka-Volterra model of phase separation

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

APA (6th Edition):

Playne, D. P. (2011). Generative programming methods for parallel partial differential field equation solvers. (Doctoral Dissertation). Massey University. Retrieved from http://hdl.handle.net/10179/2902

Chicago Manual of Style (16th Edition):

Playne, Daniel Peter. “Generative programming methods for parallel partial differential field equation solvers.” 2011. Doctoral Dissertation, Massey University. Accessed November 19, 2019. http://hdl.handle.net/10179/2902.

MLA Handbook (7th Edition):

Playne, Daniel Peter. “Generative programming methods for parallel partial differential field equation solvers.” 2011. Web. 19 Nov 2019.

Vancouver:

Playne DP. Generative programming methods for parallel partial differential field equation solvers. [Internet] [Doctoral dissertation]. Massey University; 2011. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/10179/2902.

Council of Science Editors:

Playne DP. Generative programming methods for parallel partial differential field equation solvers. [Doctoral Dissertation]. Massey University; 2011. Available from: http://hdl.handle.net/10179/2902


Technical University of Lisbon

23. Duarte, João Miguel Ferreira Couto. Para uma definição de maqueta.

Degree: 2016, Technical University of Lisbon

Tese de Doutoramento em.Arquitetura, com a especialização em Teoria e Prática de Projeto, apresentada na Faculdade de Arquitetura da Universidade de Lisboa para obtenção do… (more)

Subjects/Keywords: Maqueta; Representação; Projeto de arquitetura; Pensamento projetual; Condição genrtativa da maquete; Architectural scale model; Architetural design; Architecural design thinking; Generative condition of the scale model

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APA (6th Edition):

Duarte, J. M. F. C. (2016). Para uma definição de maqueta. (Thesis). Technical University of Lisbon. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:www.repository.utl.pt:10400.5/13708

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):

Duarte, João Miguel Ferreira Couto. “Para uma definição de maqueta.” 2016. Thesis, Technical University of Lisbon. Accessed November 19, 2019. https://www.rcaap.pt/detail.jsp?id=oai:www.repository.utl.pt:10400.5/13708.

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

MLA Handbook (7th Edition):

Duarte, João Miguel Ferreira Couto. “Para uma definição de maqueta.” 2016. Web. 19 Nov 2019.

Vancouver:

Duarte JMFC. Para uma definição de maqueta. [Internet] [Thesis]. Technical University of Lisbon; 2016. [cited 2019 Nov 19]. Available from: https://www.rcaap.pt/detail.jsp?id=oai:www.repository.utl.pt:10400.5/13708.

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

Council of Science Editors:

Duarte JMFC. Para uma definição de maqueta. [Thesis]. Technical University of Lisbon; 2016. Available from: https://www.rcaap.pt/detail.jsp?id=oai:www.repository.utl.pt:10400.5/13708

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


University of Waikato

24. Mutter, Stefan. Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation .

Degree: 2011, University of Waikato

 Detecting similarity in biological sequences is a key element to understanding the mechanisms of life. Researchers infer potential structural, functional or evolutionary relationships from similarity.… (more)

Subjects/Keywords: Machine Learning; Bioinformatics; Statistical Modelling; Hidden Markov Models; proteins; amino acids; one-class classification; propositionalisation; null model; discriminative learner; generative model; classification

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

APA (6th Edition):

Mutter, S. (2011). Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation . (Doctoral Dissertation). University of Waikato. Retrieved from http://hdl.handle.net/10289/5299

Chicago Manual of Style (16th Edition):

Mutter, Stefan. “Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation .” 2011. Doctoral Dissertation, University of Waikato. Accessed November 19, 2019. http://hdl.handle.net/10289/5299.

MLA Handbook (7th Edition):

Mutter, Stefan. “Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation .” 2011. Web. 19 Nov 2019.

Vancouver:

Mutter S. Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation . [Internet] [Doctoral dissertation]. University of Waikato; 2011. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/10289/5299.

Council of Science Editors:

Mutter S. Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation . [Doctoral Dissertation]. University of Waikato; 2011. Available from: http://hdl.handle.net/10289/5299


University of Pennsylvania

25. Huh, Jinwook. Learning Probabilistic Generative Models For Fast Sampling-Based Planning.

Degree: 2019, University of Pennsylvania

 Due to their simplicity and efficiency in high dimensional space, sampling-based motion planners have been gaining interest for robotic manipulation in recent years. We present… (more)

Subjects/Keywords: Gaussian mixture model; Machine learning; Motion and path planning; Probabilistic generative model; Robotics; Sampling-based planning; Artificial Intelligence and Robotics; Computer Sciences; Robotics

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

APA (6th Edition):

Huh, J. (2019). Learning Probabilistic Generative Models For Fast Sampling-Based Planning. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/3480

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):

Huh, Jinwook. “Learning Probabilistic Generative Models For Fast Sampling-Based Planning.” 2019. Thesis, University of Pennsylvania. Accessed November 19, 2019. https://repository.upenn.edu/edissertations/3480.

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

MLA Handbook (7th Edition):

Huh, Jinwook. “Learning Probabilistic Generative Models For Fast Sampling-Based Planning.” 2019. Web. 19 Nov 2019.

Vancouver:

Huh J. Learning Probabilistic Generative Models For Fast Sampling-Based Planning. [Internet] [Thesis]. University of Pennsylvania; 2019. [cited 2019 Nov 19]. Available from: https://repository.upenn.edu/edissertations/3480.

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

Council of Science Editors:

Huh J. Learning Probabilistic Generative Models For Fast Sampling-Based Planning. [Thesis]. University of Pennsylvania; 2019. Available from: https://repository.upenn.edu/edissertations/3480

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

26. Parish, Brandi Nicole. Creation of Chimera Through the Usage of an Inspirational System.

Degree: 2013, Texas A&M University

 My thesis involves studying the nature of chimera through history and how certain aspects of chimeras represent specific features of dualities in human nature. The… (more)

Subjects/Keywords: Chimera; chimaera; texturing; 3D model; generative; generative systems; maya; 3D; Greek; Mythology; sketch; sketching; creative

…creation of a 3D model, which takes the ideas of chimeras found in history and morphs them in new… …with the intention of creating unique chimera to be realized as a 3D model. The idea was to… …intentions. The end result would be a 3D model created with a particular purpose, a purpose that… …2013. 9 B. Generative Systems in the Service of Ideas A critical element of this project… …own work differentiates from artists I have researched in that I use the generative element… 

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APA (6th Edition):

Parish, B. N. (2013). Creation of Chimera Through the Usage of an Inspirational System. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/149460

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):

Parish, Brandi Nicole. “Creation of Chimera Through the Usage of an Inspirational System.” 2013. Thesis, Texas A&M University. Accessed November 19, 2019. http://hdl.handle.net/1969.1/149460.

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

MLA Handbook (7th Edition):

Parish, Brandi Nicole. “Creation of Chimera Through the Usage of an Inspirational System.” 2013. Web. 19 Nov 2019.

Vancouver:

Parish BN. Creation of Chimera Through the Usage of an Inspirational System. [Internet] [Thesis]. Texas A&M University; 2013. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/1969.1/149460.

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

Council of Science Editors:

Parish BN. Creation of Chimera Through the Usage of an Inspirational System. [Thesis]. Texas A&M University; 2013. Available from: http://hdl.handle.net/1969.1/149460

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


ETH Zürich

27. Tschannen, Michael Tobias. Unsupervised learning: Model-based clustering and learned compression.

Degree: 2018, ETH Zürich

Subjects/Keywords: Subspace clustering; Random processes; Spectral clustering; Lossy compression; Model compression; Generative modeling; Deep neural networks; Generative adversarial networks

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

APA (6th Edition):

Tschannen, M. T. (2018). Unsupervised learning: Model-based clustering and learned compression. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/322751

Chicago Manual of Style (16th Edition):

Tschannen, Michael Tobias. “Unsupervised learning: Model-based clustering and learned compression.” 2018. Doctoral Dissertation, ETH Zürich. Accessed November 19, 2019. http://hdl.handle.net/20.500.11850/322751.

MLA Handbook (7th Edition):

Tschannen, Michael Tobias. “Unsupervised learning: Model-based clustering and learned compression.” 2018. Web. 19 Nov 2019.

Vancouver:

Tschannen MT. Unsupervised learning: Model-based clustering and learned compression. [Internet] [Doctoral dissertation]. ETH Zürich; 2018. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/20.500.11850/322751.

Council of Science Editors:

Tschannen MT. Unsupervised learning: Model-based clustering and learned compression. [Doctoral Dissertation]. ETH Zürich; 2018. Available from: http://hdl.handle.net/20.500.11850/322751


Kyoto University / 京都大学

28. Shimonishi, Kei. Modeling and Estimation of Selection Interests through Gaze Behavior : 注視行動を用いた選択興味のモデル化及び推定.

Degree: 博士(情報学), 2017, Kyoto University / 京都大学

新制・課程博士

甲第20735号

情博第649号

Subjects/Keywords: Choice behavior; Gaze behavior; Selection interests; Generative model; Interactive decision assistance

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APA (6th Edition):

Shimonishi, K. (2017). Modeling and Estimation of Selection Interests through Gaze Behavior : 注視行動を用いた選択興味のモデル化及び推定. (Thesis). Kyoto University / 京都大学. Retrieved from http://hdl.handle.net/2433/227658 ; http://dx.doi.org/10.14989/doctor.k20735

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):

Shimonishi, Kei. “Modeling and Estimation of Selection Interests through Gaze Behavior : 注視行動を用いた選択興味のモデル化及び推定.” 2017. Thesis, Kyoto University / 京都大学. Accessed November 19, 2019. http://hdl.handle.net/2433/227658 ; http://dx.doi.org/10.14989/doctor.k20735.

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

MLA Handbook (7th Edition):

Shimonishi, Kei. “Modeling and Estimation of Selection Interests through Gaze Behavior : 注視行動を用いた選択興味のモデル化及び推定.” 2017. Web. 19 Nov 2019.

Vancouver:

Shimonishi K. Modeling and Estimation of Selection Interests through Gaze Behavior : 注視行動を用いた選択興味のモデル化及び推定. [Internet] [Thesis]. Kyoto University / 京都大学; 2017. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/2433/227658 ; http://dx.doi.org/10.14989/doctor.k20735.

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

Council of Science Editors:

Shimonishi K. Modeling and Estimation of Selection Interests through Gaze Behavior : 注視行動を用いた選択興味のモデル化及び推定. [Thesis]. Kyoto University / 京都大学; 2017. Available from: http://hdl.handle.net/2433/227658 ; http://dx.doi.org/10.14989/doctor.k20735

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


University of Pretoria

29. Smit, Willem Jacobus. Sparse coding for speech recognition.

Degree: Electrical, Electronic and Computer Engineering, 2008, University of Pretoria

 The brain is a complex organ that is computationally strong. Recent research in the field of neurobiology help scientists to better understand the working of… (more)

Subjects/Keywords: Mathematical optimization; Spike train classification; Spike train; Speech recognition; Sparse code; Linear generative model; Sparse code measurement; Dictionary training; Overcomplete dictionary; Spectrogram; UCTD

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Smit, W. J. (2008). Sparse coding for speech recognition. (Doctoral Dissertation). University of Pretoria. Retrieved from http://hdl.handle.net/2263/29409

Chicago Manual of Style (16th Edition):

Smit, Willem Jacobus. “Sparse coding for speech recognition.” 2008. Doctoral Dissertation, University of Pretoria. Accessed November 19, 2019. http://hdl.handle.net/2263/29409.

MLA Handbook (7th Edition):

Smit, Willem Jacobus. “Sparse coding for speech recognition.” 2008. Web. 19 Nov 2019.

Vancouver:

Smit WJ. Sparse coding for speech recognition. [Internet] [Doctoral dissertation]. University of Pretoria; 2008. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/2263/29409.

Council of Science Editors:

Smit WJ. Sparse coding for speech recognition. [Doctoral Dissertation]. University of Pretoria; 2008. Available from: http://hdl.handle.net/2263/29409


University of Pretoria

30. [No author]. Sparse coding for speech recognition .

Degree: 2008, University of Pretoria

 The brain is a complex organ that is computationally strong. Recent research in the field of neurobiology help scientists to better understand the working of… (more)

Subjects/Keywords: Mathematical optimization; Spike train classification; Spike train; Speech recognition; Sparse code; Linear generative model; Sparse code measurement; Dictionary training; Overcomplete dictionary; Spectrogram; UCTD

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

author], [. (2008). Sparse coding for speech recognition . (Doctoral Dissertation). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-11112008-151309/

Chicago Manual of Style (16th Edition):

author], [No. “Sparse coding for speech recognition .” 2008. Doctoral Dissertation, University of Pretoria. Accessed November 19, 2019. http://upetd.up.ac.za/thesis/available/etd-11112008-151309/.

MLA Handbook (7th Edition):

author], [No. “Sparse coding for speech recognition .” 2008. Web. 19 Nov 2019.

Vancouver:

author] [. Sparse coding for speech recognition . [Internet] [Doctoral dissertation]. University of Pretoria; 2008. [cited 2019 Nov 19]. Available from: http://upetd.up.ac.za/thesis/available/etd-11112008-151309/.

Council of Science Editors:

author] [. Sparse coding for speech recognition . [Doctoral Dissertation]. University of Pretoria; 2008. Available from: http://upetd.up.ac.za/thesis/available/etd-11112008-151309/

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