Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for subject:( Representation Learning). Showing records 1 – 30 of 296 total matches.

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Search Limiters

Last 2 Years | English Only

Department

Degrees

Levels

Languages

Country

▼ Search Limiters


University of Illinois – Urbana-Champaign

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

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

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

Subjects/Keywords: deep learning; sparse representation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Wang, Zhangyang. “Task-specific and interpretable feature learning.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed July 17, 2019. http://hdl.handle.net/2142/95560.

MLA Handbook (7th Edition):

Wang, Zhangyang. “Task-specific and interpretable feature learning.” 2016. Web. 17 Jul 2019.

Vancouver:

Wang Z. Task-specific and interpretable feature learning. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2142/95560.

Council of Science Editors:

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


Cornell University

2. Chen, Shuo. Representation Learning For Sequence And Comparison Data .

Degree: 2016, Cornell University

 The core idea of representation learning is to learn semantically more meaningful features (usually represented by a vector or vectors for each data point) from… (more)

Subjects/Keywords: Representation learning; Sequence; Pairwise comparison

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Chen, S. (2016). Representation Learning For Sequence And Comparison Data . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/43697

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):

Chen, Shuo. “Representation Learning For Sequence And Comparison Data .” 2016. Thesis, Cornell University. Accessed July 17, 2019. http://hdl.handle.net/1813/43697.

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

MLA Handbook (7th Edition):

Chen, Shuo. “Representation Learning For Sequence And Comparison Data .” 2016. Web. 17 Jul 2019.

Vancouver:

Chen S. Representation Learning For Sequence And Comparison Data . [Internet] [Thesis]. Cornell University; 2016. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/1813/43697.

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

Council of Science Editors:

Chen S. Representation Learning For Sequence And Comparison Data . [Thesis]. Cornell University; 2016. Available from: http://hdl.handle.net/1813/43697

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


University of Sydney

3. Nan, Lihao. Privacy Preserving Representation Learning For Complex Data .

Degree: 2019, University of Sydney

 Here we consider a common data encryption problem encountered by users who want to disclose some data to gain utility but preserve their private information.… (more)

Subjects/Keywords: Privacy; Data security; Representation; Learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Nan, L. (2019). Privacy Preserving Representation Learning For Complex Data . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/20662

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):

Nan, Lihao. “Privacy Preserving Representation Learning For Complex Data .” 2019. Thesis, University of Sydney. Accessed July 17, 2019. http://hdl.handle.net/2123/20662.

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

MLA Handbook (7th Edition):

Nan, Lihao. “Privacy Preserving Representation Learning For Complex Data .” 2019. Web. 17 Jul 2019.

Vancouver:

Nan L. Privacy Preserving Representation Learning For Complex Data . [Internet] [Thesis]. University of Sydney; 2019. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2123/20662.

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

Council of Science Editors:

Nan L. Privacy Preserving Representation Learning For Complex Data . [Thesis]. University of Sydney; 2019. Available from: http://hdl.handle.net/2123/20662

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

4. Han, Fangqiu. Representation Learning on Unstructured Data.

Degree: 2016, University of California – eScholarship, University of California

Representation learning, which transfers real world data such as graphs, images and texts, into representations that can be effectively processed by machine learning algorithms, has… (more)

Subjects/Keywords: Computer science; Graph; Machine Learning; Representation Learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Han, F. (2016). Representation Learning on Unstructured Data. (Thesis). University of California – eScholarship, University of California. Retrieved from http://www.escholarship.org/uc/item/0xm2125b

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, Fangqiu. “Representation Learning on Unstructured Data.” 2016. Thesis, University of California – eScholarship, University of California. Accessed July 17, 2019. http://www.escholarship.org/uc/item/0xm2125b.

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

MLA Handbook (7th Edition):

Han, Fangqiu. “Representation Learning on Unstructured Data.” 2016. Web. 17 Jul 2019.

Vancouver:

Han F. Representation Learning on Unstructured Data. [Internet] [Thesis]. University of California – eScholarship, University of California; 2016. [cited 2019 Jul 17]. Available from: http://www.escholarship.org/uc/item/0xm2125b.

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

Council of Science Editors:

Han F. Representation Learning on Unstructured Data. [Thesis]. University of California – eScholarship, University of California; 2016. Available from: http://www.escholarship.org/uc/item/0xm2125b

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


University of Alberta

5. Kiros, Ryan J. Learning Deep Representations, Embeddings and Codes from the Pixel Level of Natural and Medical Images.

Degree: MS, Department of Computing Science, 2013, University of Alberta

 Significant research has gone into engineering representations that can identify high-level semantic structure in images, such as objects, people, events and scenes. Recently there has… (more)

Subjects/Keywords: Deep Learning; Machine Learning; Representation Learning; Computer Vision

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Kiros, R. J. (2013). Learning Deep Representations, Embeddings and Codes from the Pixel Level of Natural and Medical Images. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/s1784m12w

Chicago Manual of Style (16th Edition):

Kiros, Ryan J. “Learning Deep Representations, Embeddings and Codes from the Pixel Level of Natural and Medical Images.” 2013. Masters Thesis, University of Alberta. Accessed July 17, 2019. https://era.library.ualberta.ca/files/s1784m12w.

MLA Handbook (7th Edition):

Kiros, Ryan J. “Learning Deep Representations, Embeddings and Codes from the Pixel Level of Natural and Medical Images.” 2013. Web. 17 Jul 2019.

Vancouver:

Kiros RJ. Learning Deep Representations, Embeddings and Codes from the Pixel Level of Natural and Medical Images. [Internet] [Masters thesis]. University of Alberta; 2013. [cited 2019 Jul 17]. Available from: https://era.library.ualberta.ca/files/s1784m12w.

Council of Science Editors:

Kiros RJ. Learning Deep Representations, Embeddings and Codes from the Pixel Level of Natural and Medical Images. [Masters Thesis]. University of Alberta; 2013. Available from: https://era.library.ualberta.ca/files/s1784m12w


Delft University of Technology

6. Munk, J. Deep Reinforcement Learning - Pretraining actor-critic networks using state representation learning:.

Degree: 2016, Delft University of Technology

 In control, the objective is to find a mapping from states to actions that steer a system to a desired reference. A controller can be… (more)

Subjects/Keywords: Deep Learning; Reinforcement Learning; State Representation Learning; Optimal Control; Artificial Intelligence

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Munk, J. (2016). Deep Reinforcement Learning - Pretraining actor-critic networks using state representation learning:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5685a3bd-c278-4a1b-a372-da28822cf140

Chicago Manual of Style (16th Edition):

Munk, J. “Deep Reinforcement Learning - Pretraining actor-critic networks using state representation learning:.” 2016. Masters Thesis, Delft University of Technology. Accessed July 17, 2019. http://resolver.tudelft.nl/uuid:5685a3bd-c278-4a1b-a372-da28822cf140.

MLA Handbook (7th Edition):

Munk, J. “Deep Reinforcement Learning - Pretraining actor-critic networks using state representation learning:.” 2016. Web. 17 Jul 2019.

Vancouver:

Munk J. Deep Reinforcement Learning - Pretraining actor-critic networks using state representation learning:. [Internet] [Masters thesis]. Delft University of Technology; 2016. [cited 2019 Jul 17]. Available from: http://resolver.tudelft.nl/uuid:5685a3bd-c278-4a1b-a372-da28822cf140.

Council of Science Editors:

Munk J. Deep Reinforcement Learning - Pretraining actor-critic networks using state representation learning:. [Masters Thesis]. Delft University of Technology; 2016. Available from: http://resolver.tudelft.nl/uuid:5685a3bd-c278-4a1b-a372-da28822cf140


Northeastern University

7. Doty, Kevin. Representation Learning For Control.

Degree: MS, Department of Electrical and Computer Engineering, 2019, Northeastern University

 State representation learning finds an embedding from a high dimensional observation space to a lower dimensional and information dense state space, without supervision. Effective state… (more)

Subjects/Keywords: Machine Learning; Reinforcement Learning; Representation Learning; State Abstraction; Computer engineering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Doty, K. (2019). Representation Learning For Control. (Masters Thesis). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20317893

Chicago Manual of Style (16th Edition):

Doty, Kevin. “Representation Learning For Control.” 2019. Masters Thesis, Northeastern University. Accessed July 17, 2019. http://hdl.handle.net/2047/D20317893.

MLA Handbook (7th Edition):

Doty, Kevin. “Representation Learning For Control.” 2019. Web. 17 Jul 2019.

Vancouver:

Doty K. Representation Learning For Control. [Internet] [Masters thesis]. Northeastern University; 2019. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2047/D20317893.

Council of Science Editors:

Doty K. Representation Learning For Control. [Masters Thesis]. Northeastern University; 2019. Available from: http://hdl.handle.net/2047/D20317893


University of Edinburgh

8. Whiteley, Anna. Semantic representations and additional material in facilitating learning words in the less preferred modality of deaf children.

Degree: 2006, University of Edinburgh

 Four deaf children’s (mean age = 10 years 10 months) semantic representations of particular vocabulary items were explored in this study. It was intended to… (more)

Subjects/Keywords: semantic representation; preferred modality; BSL; word learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Whiteley, A. (2006). Semantic representations and additional material in facilitating learning words in the less preferred modality of deaf children. (Thesis). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/2360

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):

Whiteley, Anna. “Semantic representations and additional material in facilitating learning words in the less preferred modality of deaf children.” 2006. Thesis, University of Edinburgh. Accessed July 17, 2019. http://hdl.handle.net/1842/2360.

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

MLA Handbook (7th Edition):

Whiteley, Anna. “Semantic representations and additional material in facilitating learning words in the less preferred modality of deaf children.” 2006. Web. 17 Jul 2019.

Vancouver:

Whiteley A. Semantic representations and additional material in facilitating learning words in the less preferred modality of deaf children. [Internet] [Thesis]. University of Edinburgh; 2006. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/1842/2360.

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

Council of Science Editors:

Whiteley A. Semantic representations and additional material in facilitating learning words in the less preferred modality of deaf children. [Thesis]. University of Edinburgh; 2006. Available from: http://hdl.handle.net/1842/2360

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


Victoria University of Wellington

9. Clarke, Adam. Representing Qualitative Action Models for Learning in Complex Virtual Worlds.

Degree: 2011, Victoria University of Wellington

 This thesis addresses the problem of representing and learning qualitative models of behaviour in complex virtual worlds. It presents a novel representation, ‘Q-Systems’, that integrates… (more)

Subjects/Keywords: Artificial intelligence; Autonomous learning; Knowledge representation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Clarke, A. (2011). Representing Qualitative Action Models for Learning in Complex Virtual Worlds. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/1925

Chicago Manual of Style (16th Edition):

Clarke, Adam. “Representing Qualitative Action Models for Learning in Complex Virtual Worlds.” 2011. Masters Thesis, Victoria University of Wellington. Accessed July 17, 2019. http://hdl.handle.net/10063/1925.

MLA Handbook (7th Edition):

Clarke, Adam. “Representing Qualitative Action Models for Learning in Complex Virtual Worlds.” 2011. Web. 17 Jul 2019.

Vancouver:

Clarke A. Representing Qualitative Action Models for Learning in Complex Virtual Worlds. [Internet] [Masters thesis]. Victoria University of Wellington; 2011. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/10063/1925.

Council of Science Editors:

Clarke A. Representing Qualitative Action Models for Learning in Complex Virtual Worlds. [Masters Thesis]. Victoria University of Wellington; 2011. Available from: http://hdl.handle.net/10063/1925


Kent State University

10. Hartin, Travis L. REPRESENTATIONAL INERTIA IN PRESCHOOLERS’ OBJECT LABEL LEARNING.

Degree: MBA, College of Arts and Sciences / Department of Psychology, 2011, Kent State University

 Preschoolers’ object label generalization was hypothesized to be influenced by the representation that they form for an object before a label is introduced for it.… (more)

Subjects/Keywords: Developmental Psychology; preschooler; representation; word learning; language

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Hartin, T. L. (2011). REPRESENTATIONAL INERTIA IN PRESCHOOLERS’ OBJECT LABEL LEARNING. (Masters Thesis). Kent State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=kent1322573040

Chicago Manual of Style (16th Edition):

Hartin, Travis L. “REPRESENTATIONAL INERTIA IN PRESCHOOLERS’ OBJECT LABEL LEARNING.” 2011. Masters Thesis, Kent State University. Accessed July 17, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1322573040.

MLA Handbook (7th Edition):

Hartin, Travis L. “REPRESENTATIONAL INERTIA IN PRESCHOOLERS’ OBJECT LABEL LEARNING.” 2011. Web. 17 Jul 2019.

Vancouver:

Hartin TL. REPRESENTATIONAL INERTIA IN PRESCHOOLERS’ OBJECT LABEL LEARNING. [Internet] [Masters thesis]. Kent State University; 2011. [cited 2019 Jul 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=kent1322573040.

Council of Science Editors:

Hartin TL. REPRESENTATIONAL INERTIA IN PRESCHOOLERS’ OBJECT LABEL LEARNING. [Masters Thesis]. Kent State University; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=kent1322573040


University of Alberta

11. Das Gupta, Ujjwal. Adaptive Representation for Policy Gradient.

Degree: MS, Department of Computing Science, 2015, University of Alberta

 Much of the focus on finding good representations in reinforcement learning has been on learning complex non-linear predictors of value. Methods like policy gradient, that… (more)

Subjects/Keywords: Representation Learning; Decision Trees; Policy Gradient; Reinforcement Learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Das Gupta, U. (2015). Adaptive Representation for Policy Gradient. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/zk51vk289

Chicago Manual of Style (16th Edition):

Das Gupta, Ujjwal. “Adaptive Representation for Policy Gradient.” 2015. Masters Thesis, University of Alberta. Accessed July 17, 2019. https://era.library.ualberta.ca/files/zk51vk289.

MLA Handbook (7th Edition):

Das Gupta, Ujjwal. “Adaptive Representation for Policy Gradient.” 2015. Web. 17 Jul 2019.

Vancouver:

Das Gupta U. Adaptive Representation for Policy Gradient. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2019 Jul 17]. Available from: https://era.library.ualberta.ca/files/zk51vk289.

Council of Science Editors:

Das Gupta U. Adaptive Representation for Policy Gradient. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/zk51vk289


University of Colorado

12. Canas, Fabian Francisco. Selective Attention as an Example of Building Representations within Reinforcement Learning.

Degree: MA, Psychology & Neuroscience, 2011, University of Colorado

  Humans demonstrate an incredible capacity to learn novel tasks in complex dynamic environments. Reinforcement learning (RL) has shown promise as a computational framework for… (more)

Subjects/Keywords: Attention; Modeling; Reinforcement Learning; Representation Learning; Cognitive Psychology; Computer Sciences

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Canas, F. F. (2011). Selective Attention as an Example of Building Representations within Reinforcement Learning. (Masters Thesis). University of Colorado. Retrieved from http://scholar.colorado.edu/psyc_gradetds/17

Chicago Manual of Style (16th Edition):

Canas, Fabian Francisco. “Selective Attention as an Example of Building Representations within Reinforcement Learning.” 2011. Masters Thesis, University of Colorado. Accessed July 17, 2019. http://scholar.colorado.edu/psyc_gradetds/17.

MLA Handbook (7th Edition):

Canas, Fabian Francisco. “Selective Attention as an Example of Building Representations within Reinforcement Learning.” 2011. Web. 17 Jul 2019.

Vancouver:

Canas FF. Selective Attention as an Example of Building Representations within Reinforcement Learning. [Internet] [Masters thesis]. University of Colorado; 2011. [cited 2019 Jul 17]. Available from: http://scholar.colorado.edu/psyc_gradetds/17.

Council of Science Editors:

Canas FF. Selective Attention as an Example of Building Representations within Reinforcement Learning. [Masters Thesis]. University of Colorado; 2011. Available from: http://scholar.colorado.edu/psyc_gradetds/17

13. Silberer, Carina Helga. Learning visually grounded meaning representations.

Degree: PhD, 2015, University of Edinburgh

 Humans possess a rich semantic knowledge of words and concepts which captures the perceivable physical properties of their real-world referents and their relations. Encoding this… (more)

Subjects/Keywords: 006.3; lexical semantics; grounding; vision and language; deep learning; representation learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Silberer, C. H. (2015). Learning visually grounded meaning representations. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/14236

Chicago Manual of Style (16th Edition):

Silberer, Carina Helga. “Learning visually grounded meaning representations.” 2015. Doctoral Dissertation, University of Edinburgh. Accessed July 17, 2019. http://hdl.handle.net/1842/14236.

MLA Handbook (7th Edition):

Silberer, Carina Helga. “Learning visually grounded meaning representations.” 2015. Web. 17 Jul 2019.

Vancouver:

Silberer CH. Learning visually grounded meaning representations. [Internet] [Doctoral dissertation]. University of Edinburgh; 2015. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/1842/14236.

Council of Science Editors:

Silberer CH. Learning visually grounded meaning representations. [Doctoral Dissertation]. University of Edinburgh; 2015. Available from: http://hdl.handle.net/1842/14236


University of Manchester

14. Parkinson, Jon Charles. Representation learning with a temporally coherent mixed-representation.

Degree: 2017, University of Manchester

Guiding a representation towards capturing temporally coherent aspects present invideo improves object identity encoding. Existing models apply temporal coherenceuniformly over all features based on the… (more)

Subjects/Keywords: Representation learning; Neural Networks; Temporal coherence; Autoencoders; Unsupervised learning; Computer vision

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Parkinson, J. C. (2017). Representation learning with a temporally coherent mixed-representation. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:308982

Chicago Manual of Style (16th Edition):

Parkinson, Jon Charles. “Representation learning with a temporally coherent mixed-representation.” 2017. Doctoral Dissertation, University of Manchester. Accessed July 17, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:308982.

MLA Handbook (7th Edition):

Parkinson, Jon Charles. “Representation learning with a temporally coherent mixed-representation.” 2017. Web. 17 Jul 2019.

Vancouver:

Parkinson JC. Representation learning with a temporally coherent mixed-representation. [Internet] [Doctoral dissertation]. University of Manchester; 2017. [cited 2019 Jul 17]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:308982.

Council of Science Editors:

Parkinson JC. Representation learning with a temporally coherent mixed-representation. [Doctoral Dissertation]. University of Manchester; 2017. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:308982


Wayne State University

15. Mahnaz, Faria. Effective Auto Encoder For Unsupervised Sparse Representation.

Degree: MS, Computer Science, 2015, Wayne State University

  High dimensionality and the sheer size of unlabeled data available today demand new development in unsupervised learning of sparse representation. Despite of recent advances… (more)

Subjects/Keywords: Machine Learning; Object classification; Optimization; Representation learning; Computer Sciences

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Mahnaz, F. (2015). Effective Auto Encoder For Unsupervised Sparse Representation. (Masters Thesis). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_theses/427

Chicago Manual of Style (16th Edition):

Mahnaz, Faria. “Effective Auto Encoder For Unsupervised Sparse Representation.” 2015. Masters Thesis, Wayne State University. Accessed July 17, 2019. https://digitalcommons.wayne.edu/oa_theses/427.

MLA Handbook (7th Edition):

Mahnaz, Faria. “Effective Auto Encoder For Unsupervised Sparse Representation.” 2015. Web. 17 Jul 2019.

Vancouver:

Mahnaz F. Effective Auto Encoder For Unsupervised Sparse Representation. [Internet] [Masters thesis]. Wayne State University; 2015. [cited 2019 Jul 17]. Available from: https://digitalcommons.wayne.edu/oa_theses/427.

Council of Science Editors:

Mahnaz F. Effective Auto Encoder For Unsupervised Sparse Representation. [Masters Thesis]. Wayne State University; 2015. Available from: https://digitalcommons.wayne.edu/oa_theses/427


Princeton University

16. Stachenfeld, Kimberly Lauren. Learning Neural Representations That Support Efficient Reinforcement Learning .

Degree: PhD, 2018, Princeton University

 RL has been transformative for neuroscience by providing a normative anchor for interpreting neural and behavioral data. End-to-end RL methods have scored impressive victories with… (more)

Subjects/Keywords: Grid Cell; Hippocampus; Place Cell; Reinforcement Learning; Representation Learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Stachenfeld, K. L. (2018). Learning Neural Representations That Support Efficient Reinforcement Learning . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01qb98mj16v

Chicago Manual of Style (16th Edition):

Stachenfeld, Kimberly Lauren. “Learning Neural Representations That Support Efficient Reinforcement Learning .” 2018. Doctoral Dissertation, Princeton University. Accessed July 17, 2019. http://arks.princeton.edu/ark:/88435/dsp01qb98mj16v.

MLA Handbook (7th Edition):

Stachenfeld, Kimberly Lauren. “Learning Neural Representations That Support Efficient Reinforcement Learning .” 2018. Web. 17 Jul 2019.

Vancouver:

Stachenfeld KL. Learning Neural Representations That Support Efficient Reinforcement Learning . [Internet] [Doctoral dissertation]. Princeton University; 2018. [cited 2019 Jul 17]. Available from: http://arks.princeton.edu/ark:/88435/dsp01qb98mj16v.

Council of Science Editors:

Stachenfeld KL. Learning Neural Representations That Support Efficient Reinforcement Learning . [Doctoral Dissertation]. Princeton University; 2018. Available from: http://arks.princeton.edu/ark:/88435/dsp01qb98mj16v


NSYSU

17. Kuo, Bo-Wen. Interpretable representation learning based on Deep Rule Forests.

Degree: Master, Information Management, 2018, NSYSU

 The spirit of tree-based methods is to learn rules. A large number of machine learning techniques are tree-based. More complicated tree learners may result in… (more)

Subjects/Keywords: Rule Learning; Random Forest; Representation Learning; Interpretability; Deep Rule Forest

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Kuo, B. (2018). Interpretable representation learning based on Deep Rule Forests. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727118-134901

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):

Kuo, Bo-Wen. “Interpretable representation learning based on Deep Rule Forests.” 2018. Thesis, NSYSU. Accessed July 17, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727118-134901.

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

MLA Handbook (7th Edition):

Kuo, Bo-Wen. “Interpretable representation learning based on Deep Rule Forests.” 2018. Web. 17 Jul 2019.

Vancouver:

Kuo B. Interpretable representation learning based on Deep Rule Forests. [Internet] [Thesis]. NSYSU; 2018. [cited 2019 Jul 17]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727118-134901.

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

Council of Science Editors:

Kuo B. Interpretable representation learning based on Deep Rule Forests. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727118-134901

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


Washington State University

18. [No author]. Proximal and Distal Effects in Action Representation .

Degree: 2013, Washington State University

 The current study examined whether an action plan based on stimulus discrimination can be represented by action features corresponding to the perceptual effects of an… (more)

Subjects/Keywords: Psychology; action features; action planning; action representation; ideomotor learning; sensorimotor learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

author], [. (2013). Proximal and Distal Effects in Action Representation . (Thesis). Washington State University. Retrieved from http://hdl.handle.net/2376/4925

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):

author], [No. “Proximal and Distal Effects in Action Representation .” 2013. Thesis, Washington State University. Accessed July 17, 2019. http://hdl.handle.net/2376/4925.

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

MLA Handbook (7th Edition):

author], [No. “Proximal and Distal Effects in Action Representation .” 2013. Web. 17 Jul 2019.

Vancouver:

author] [. Proximal and Distal Effects in Action Representation . [Internet] [Thesis]. Washington State University; 2013. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2376/4925.

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

Council of Science Editors:

author] [. Proximal and Distal Effects in Action Representation . [Thesis]. Washington State University; 2013. Available from: http://hdl.handle.net/2376/4925

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


University of Wollongong

19. Yang, Jie. A machine learning paradigm based on sparse signal representation.

Degree: PhD, 2013, University of Wollongong

  Machine learning has been extensively investigated over the last three decades for its capability to learn mapping of functions from patterns. Nowadays, machine learning(more)

Subjects/Keywords: sparse signal representation; compressed sensing; machine learning; model selection; dictionary learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Yang, J. (2013). A machine learning paradigm based on sparse signal representation. (Doctoral Dissertation). University of Wollongong. Retrieved from 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, 0906 ELECTRICAL AND ELECTRONIC ENGINEERING ; https://ro.uow.edu.au/theses/3898

Chicago Manual of Style (16th Edition):

Yang, Jie. “A machine learning paradigm based on sparse signal representation.” 2013. Doctoral Dissertation, University of Wollongong. Accessed July 17, 2019. 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, 0906 ELECTRICAL AND ELECTRONIC ENGINEERING ; https://ro.uow.edu.au/theses/3898.

MLA Handbook (7th Edition):

Yang, Jie. “A machine learning paradigm based on sparse signal representation.” 2013. Web. 17 Jul 2019.

Vancouver:

Yang J. A machine learning paradigm based on sparse signal representation. [Internet] [Doctoral dissertation]. University of Wollongong; 2013. [cited 2019 Jul 17]. Available from: 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, 0906 ELECTRICAL AND ELECTRONIC ENGINEERING ; https://ro.uow.edu.au/theses/3898.

Council of Science Editors:

Yang J. A machine learning paradigm based on sparse signal representation. [Doctoral Dissertation]. University of Wollongong; 2013. Available from: 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, 0906 ELECTRICAL AND ELECTRONIC ENGINEERING ; https://ro.uow.edu.au/theses/3898


Rochester Institute of Technology

20. Graham, Dillon R. Holographic Generative Memory: Neurally Inspired One-Shot Learning with Memory Augmented Neural Networks.

Degree: MS, Computer Engineering, 2018, Rochester Institute of Technology

  Humans quickly parse and categorize stimuli by combining perceptual information and previously learned knowledge. We are capable of learning new information quickly with only… (more)

Subjects/Keywords: Artificial neural networks; Deep learning; Holographic reduced representation; One-shot learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Graham, D. R. (2018). Holographic Generative Memory: Neurally Inspired One-Shot Learning with Memory Augmented Neural Networks. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9866

Chicago Manual of Style (16th Edition):

Graham, Dillon R. “Holographic Generative Memory: Neurally Inspired One-Shot Learning with Memory Augmented Neural Networks.” 2018. Masters Thesis, Rochester Institute of Technology. Accessed July 17, 2019. https://scholarworks.rit.edu/theses/9866.

MLA Handbook (7th Edition):

Graham, Dillon R. “Holographic Generative Memory: Neurally Inspired One-Shot Learning with Memory Augmented Neural Networks.” 2018. Web. 17 Jul 2019.

Vancouver:

Graham DR. Holographic Generative Memory: Neurally Inspired One-Shot Learning with Memory Augmented Neural Networks. [Internet] [Masters thesis]. Rochester Institute of Technology; 2018. [cited 2019 Jul 17]. Available from: https://scholarworks.rit.edu/theses/9866.

Council of Science Editors:

Graham DR. Holographic Generative Memory: Neurally Inspired One-Shot Learning with Memory Augmented Neural Networks. [Masters Thesis]. Rochester Institute of Technology; 2018. Available from: https://scholarworks.rit.edu/theses/9866


West Virginia University

21. Yadav, Daksha. On Matching Faces with Temporal Variations using Representation Learning.

Degree: PhD, Lane Department of Computer Science and Electrical Engineering, 2019, West Virginia University

  Developing automatic face recognition algorithms which are robust to intra-subject variations is a challenging research problem in the computer vision research community. Apart from… (more)

Subjects/Keywords: Face recognition; Machine learning; Temporal Variations; Representation Learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Yadav, D. (2019). On Matching Faces with Temporal Variations using Representation Learning. (Doctoral Dissertation). West Virginia University. Retrieved from https://researchrepository.wvu.edu/etd/3939

Chicago Manual of Style (16th Edition):

Yadav, Daksha. “On Matching Faces with Temporal Variations using Representation Learning.” 2019. Doctoral Dissertation, West Virginia University. Accessed July 17, 2019. https://researchrepository.wvu.edu/etd/3939.

MLA Handbook (7th Edition):

Yadav, Daksha. “On Matching Faces with Temporal Variations using Representation Learning.” 2019. Web. 17 Jul 2019.

Vancouver:

Yadav D. On Matching Faces with Temporal Variations using Representation Learning. [Internet] [Doctoral dissertation]. West Virginia University; 2019. [cited 2019 Jul 17]. Available from: https://researchrepository.wvu.edu/etd/3939.

Council of Science Editors:

Yadav D. On Matching Faces with Temporal Variations using Representation Learning. [Doctoral Dissertation]. West Virginia University; 2019. Available from: https://researchrepository.wvu.edu/etd/3939


University of Illinois – Urbana-Champaign

22. Wen, Bihan. Transform learning based image and video processing.

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

 In recent years, sparse signal modeling, especially using the synthesis dictionary model, has received much attention. Sparse coding in the synthesis model is, however, NP-hard.… (more)

Subjects/Keywords: Dictionary Learning; Transform Learning; Sparse Representation; Online Learning; Image Representation; Image Denoising; Video Denoising; Image Segmentation; Clustering; Machine Learning; Overcomplete Representation; Big Data; Inverse Problem

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Wen, B. (2015). Transform learning based image and video processing. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/88970

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):

Wen, Bihan. “Transform learning based image and video processing.” 2015. Thesis, University of Illinois – Urbana-Champaign. Accessed July 17, 2019. http://hdl.handle.net/2142/88970.

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

MLA Handbook (7th Edition):

Wen, Bihan. “Transform learning based image and video processing.” 2015. Web. 17 Jul 2019.

Vancouver:

Wen B. Transform learning based image and video processing. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2142/88970.

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

Council of Science Editors:

Wen B. Transform learning based image and video processing. [Thesis]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/88970

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


KTH

23. Lau, Kiu Wai. Representation Learning on Brain MR Images for Tumor Segmentation.

Degree: Biotechnology and Health (CBH), 2018, KTH

  MRI is favorable for brain imaging due to its excellent soft tissue contrast and absence of harmful ionizing radiation. Many have proposed supervised multimodal… (more)

Subjects/Keywords: MRI; brain tumor; representation learning; segmentation; autoencoder; unified representation; Medical Engineering; Medicinteknik

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Lau, K. W. (2018). Representation Learning on Brain MR Images for Tumor Segmentation. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234827

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):

Lau, Kiu Wai. “Representation Learning on Brain MR Images for Tumor Segmentation.” 2018. Thesis, KTH. Accessed July 17, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234827.

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

MLA Handbook (7th Edition):

Lau, Kiu Wai. “Representation Learning on Brain MR Images for Tumor Segmentation.” 2018. Web. 17 Jul 2019.

Vancouver:

Lau KW. Representation Learning on Brain MR Images for Tumor Segmentation. [Internet] [Thesis]. KTH; 2018. [cited 2019 Jul 17]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234827.

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

Council of Science Editors:

Lau KW. Representation Learning on Brain MR Images for Tumor Segmentation. [Thesis]. KTH; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234827

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


University of California – Berkeley

24. Livezey, Jesse. Learning and Inferring Representations of Data in Neural Networks.

Degree: Physics, 2017, University of California – Berkeley

 Finding useful representations of data in order to facilitate scientific knowledge generation is a ubiquitous concept across disciplines. Until the development of machine learning and… (more)

Subjects/Keywords: Biophysics; Statistics; Neurosciences; deep learning; machine learning; neural networks; neuroscience; representation learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Livezey, J. (2017). Learning and Inferring Representations of Data in Neural Networks. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/1vj6j2rx

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):

Livezey, Jesse. “Learning and Inferring Representations of Data in Neural Networks.” 2017. Thesis, University of California – Berkeley. Accessed July 17, 2019. http://www.escholarship.org/uc/item/1vj6j2rx.

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

MLA Handbook (7th Edition):

Livezey, Jesse. “Learning and Inferring Representations of Data in Neural Networks.” 2017. Web. 17 Jul 2019.

Vancouver:

Livezey J. Learning and Inferring Representations of Data in Neural Networks. [Internet] [Thesis]. University of California – Berkeley; 2017. [cited 2019 Jul 17]. Available from: http://www.escholarship.org/uc/item/1vj6j2rx.

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

Council of Science Editors:

Livezey J. Learning and Inferring Representations of Data in Neural Networks. [Thesis]. University of California – Berkeley; 2017. Available from: http://www.escholarship.org/uc/item/1vj6j2rx

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


University of Michigan

25. Sohn, Kihyuk. Improving Deep Representation Learning with Complex and Multimodal Data.

Degree: PhD, Electrical Engineering: Systems, 2015, University of Michigan

Representation learning has emerged as a way to learn meaningful representation from data and made a breakthrough in many applications including visual object recognition, speech… (more)

Subjects/Keywords: deep learning; representation learning; structured output prediction; multimodal learning; Electrical Engineering; Engineering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Sohn, K. (2015). Improving Deep Representation Learning with Complex and Multimodal Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113549

Chicago Manual of Style (16th Edition):

Sohn, Kihyuk. “Improving Deep Representation Learning with Complex and Multimodal Data.” 2015. Doctoral Dissertation, University of Michigan. Accessed July 17, 2019. http://hdl.handle.net/2027.42/113549.

MLA Handbook (7th Edition):

Sohn, Kihyuk. “Improving Deep Representation Learning with Complex and Multimodal Data.” 2015. Web. 17 Jul 2019.

Vancouver:

Sohn K. Improving Deep Representation Learning with Complex and Multimodal Data. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2027.42/113549.

Council of Science Editors:

Sohn K. Improving Deep Representation Learning with Complex and Multimodal Data. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113549


University of Manchester

26. Wang, Qian. ZERO-SHOT VISUAL RECOGNITION VIA LATENT EMBEDDING LEARNING.

Degree: 2018, University of Manchester

 Traditional supervised visual recognition methods require a great number of annotated examples for each concerned class. The collection and annotation of visual data (e.g., images… (more)

Subjects/Keywords: Zero-shot learning; Human action recognition; Object recognition; Semantic representation; Multi-label learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Wang, Q. (2018). ZERO-SHOT VISUAL RECOGNITION VIA LATENT EMBEDDING LEARNING. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312951

Chicago Manual of Style (16th Edition):

Wang, Qian. “ZERO-SHOT VISUAL RECOGNITION VIA LATENT EMBEDDING LEARNING.” 2018. Doctoral Dissertation, University of Manchester. Accessed July 17, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312951.

MLA Handbook (7th Edition):

Wang, Qian. “ZERO-SHOT VISUAL RECOGNITION VIA LATENT EMBEDDING LEARNING.” 2018. Web. 17 Jul 2019.

Vancouver:

Wang Q. ZERO-SHOT VISUAL RECOGNITION VIA LATENT EMBEDDING LEARNING. [Internet] [Doctoral dissertation]. University of Manchester; 2018. [cited 2019 Jul 17]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312951.

Council of Science Editors:

Wang Q. ZERO-SHOT VISUAL RECOGNITION VIA LATENT EMBEDDING LEARNING. [Doctoral Dissertation]. University of Manchester; 2018. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312951


Duke University

27. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 July 17, 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. 17 Jul 2019.

Vancouver:

Pu Y. Deep Generative Models for Image Representation Learning . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Jul 17]. 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


University of Houston

28. Shrestha, Prasha 1987-. Authorship Attribution for Realistic Scenarios.

Degree: Computer Science, Department of, 2018, University of Houston

 A majority of the previous works on authorship attribution make several assumptions while designing their problem. They assume that the candidate author set size is… (more)

Subjects/Keywords: authorship attribution; domain adaptation; deep learning; representation learning; embeddings; CNN for NLP

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Shrestha, P. 1. (2018). Authorship Attribution for Realistic Scenarios. (Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3472

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):

Shrestha, Prasha 1987-. “Authorship Attribution for Realistic Scenarios.” 2018. Thesis, University of Houston. Accessed July 17, 2019. http://hdl.handle.net/10657/3472.

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

MLA Handbook (7th Edition):

Shrestha, Prasha 1987-. “Authorship Attribution for Realistic Scenarios.” 2018. Web. 17 Jul 2019.

Vancouver:

Shrestha P1. Authorship Attribution for Realistic Scenarios. [Internet] [Thesis]. University of Houston; 2018. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/10657/3472.

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

Council of Science Editors:

Shrestha P1. Authorship Attribution for Realistic Scenarios. [Thesis]. University of Houston; 2018. Available from: http://hdl.handle.net/10657/3472

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


University of Manchester

29. Wang, Qian. Zero-shot visual recognition via latent embedding learning.

Degree: PhD, 2018, University of Manchester

 Traditional supervised visual recognition methods require a great number of annotated examples for each concerned class. The collection and annotation of visual data (e.g., images… (more)

Subjects/Keywords: 004; Semantic representation; Zero-shot learning; Human action recognition; Object recognition; Multi-label learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Wang, Q. (2018). Zero-shot visual recognition via latent embedding learning. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/zeroshot-visual-recognition-via-latent-embedding-learning(bec510af-6a53-4114-9407-75212e1a08e1).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740350

Chicago Manual of Style (16th Edition):

Wang, Qian. “Zero-shot visual recognition via latent embedding learning.” 2018. Doctoral Dissertation, University of Manchester. Accessed July 17, 2019. https://www.research.manchester.ac.uk/portal/en/theses/zeroshot-visual-recognition-via-latent-embedding-learning(bec510af-6a53-4114-9407-75212e1a08e1).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740350.

MLA Handbook (7th Edition):

Wang, Qian. “Zero-shot visual recognition via latent embedding learning.” 2018. Web. 17 Jul 2019.

Vancouver:

Wang Q. Zero-shot visual recognition via latent embedding learning. [Internet] [Doctoral dissertation]. University of Manchester; 2018. [cited 2019 Jul 17]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/zeroshot-visual-recognition-via-latent-embedding-learning(bec510af-6a53-4114-9407-75212e1a08e1).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740350.

Council of Science Editors:

Wang Q. Zero-shot visual recognition via latent embedding learning. [Doctoral Dissertation]. University of Manchester; 2018. Available from: https://www.research.manchester.ac.uk/portal/en/theses/zeroshot-visual-recognition-via-latent-embedding-learning(bec510af-6a53-4114-9407-75212e1a08e1).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740350

30. Wang, Chang. A Geometric Framework for Transfer Learning Using Manifold Alignment.

Degree: PhD, Computer Science, 2010, U of Massachusetts : PhD

 Many machine learning problems involve dealing with a large amount of high-dimensional data across diverse domains. In addition, annotating or labeling the data is expensive… (more)

Subjects/Keywords: Dimensionality Reduction; Manifold Alignment; Multiscale Analysis; Representation Learning; Topic Model; Transfer Learning; Computer Sciences

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Wang, C. (2010). A Geometric Framework for Transfer Learning Using Manifold Alignment. (Doctoral Dissertation). U of Massachusetts : PhD. Retrieved from https://scholarworks.umass.edu/open_access_dissertations/269

Chicago Manual of Style (16th Edition):

Wang, Chang. “A Geometric Framework for Transfer Learning Using Manifold Alignment.” 2010. Doctoral Dissertation, U of Massachusetts : PhD. Accessed July 17, 2019. https://scholarworks.umass.edu/open_access_dissertations/269.

MLA Handbook (7th Edition):

Wang, Chang. “A Geometric Framework for Transfer Learning Using Manifold Alignment.” 2010. Web. 17 Jul 2019.

Vancouver:

Wang C. A Geometric Framework for Transfer Learning Using Manifold Alignment. [Internet] [Doctoral dissertation]. U of Massachusetts : PhD; 2010. [cited 2019 Jul 17]. Available from: https://scholarworks.umass.edu/open_access_dissertations/269.

Council of Science Editors:

Wang C. A Geometric Framework for Transfer Learning Using Manifold Alignment. [Doctoral Dissertation]. U of Massachusetts : PhD; 2010. Available from: https://scholarworks.umass.edu/open_access_dissertations/269

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

.