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:(Domain Transfer Learning). Showing records 1 – 30 of 44 total matches.

[1] [2]

Search Limiters

Last 2 Years | English Only

▼ Search Limiters


Duke University

1. Li, Yitong. Learning to Transfer Knowledge from Multiple Sources of Electrophysiological Signals .

Degree: 2020, Duke University

  Deep learning methods have shown unparalleled performance when trained on vast amounts of diverse labeled training data, often collected at great cost. In many… (more)

Subjects/Keywords: Computer science; domain adaptation; domain generalization; EEG/LFP; transfer learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Li, Y. (2020). Learning to Transfer Knowledge from Multiple Sources of Electrophysiological Signals . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/20864

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

Chicago Manual of Style (16th Edition):

Li, Yitong. “Learning to Transfer Knowledge from Multiple Sources of Electrophysiological Signals .” 2020. Thesis, Duke University. Accessed May 09, 2021. http://hdl.handle.net/10161/20864.

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

MLA Handbook (7th Edition):

Li, Yitong. “Learning to Transfer Knowledge from Multiple Sources of Electrophysiological Signals .” 2020. Web. 09 May 2021.

Vancouver:

Li Y. Learning to Transfer Knowledge from Multiple Sources of Electrophysiological Signals . [Internet] [Thesis]. Duke University; 2020. [cited 2021 May 09]. Available from: http://hdl.handle.net/10161/20864.

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

Council of Science Editors:

Li Y. Learning to Transfer Knowledge from Multiple Sources of Electrophysiological Signals . [Thesis]. Duke University; 2020. Available from: http://hdl.handle.net/10161/20864

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


University of Tennessee – Knoxville

2. Song, Yang. Cross domain Image Transformation and Generation by Deep Learning.

Degree: 2019, University of Tennessee – Knoxville

 Compared with single domain learning, cross-domain learning is more challenging due to the large domain variation. In addition, cross-domain image synthesis is more difficult than… (more)

Subjects/Keywords: Generative model; cross-domain; domain transfer; deep learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Song, Y. (2019). Cross domain Image Transformation and Generation by Deep Learning. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/5368

Chicago Manual of Style (16th Edition):

Song, Yang. “Cross domain Image Transformation and Generation by Deep Learning.” 2019. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed May 09, 2021. https://trace.tennessee.edu/utk_graddiss/5368.

MLA Handbook (7th Edition):

Song, Yang. “Cross domain Image Transformation and Generation by Deep Learning.” 2019. Web. 09 May 2021.

Vancouver:

Song Y. Cross domain Image Transformation and Generation by Deep Learning. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2019. [cited 2021 May 09]. Available from: https://trace.tennessee.edu/utk_graddiss/5368.

Council of Science Editors:

Song Y. Cross domain Image Transformation and Generation by Deep Learning. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2019. Available from: https://trace.tennessee.edu/utk_graddiss/5368


Delft University of Technology

3. Yin, Z. (author). Assessment of Parkinson's Disease Severity from Videos using Deep Architectures.

Degree: 2020, Delft University of Technology

 Parkinson's disease (PD) diagnosis is based on clinical criteria, i.e. bradykinesia, rest tremor, rigidity, etc. Assessment of the severity of PD symptoms, however, is subject(more)

Subjects/Keywords: Parkinson's Disease; Deep learning; Transfer learning; Self-attention; Multi-domain learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Yin, Z. (. (2020). Assessment of Parkinson's Disease Severity from Videos using Deep Architectures. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a0336a50-d169-45cb-abe7-097ba8d15084

Chicago Manual of Style (16th Edition):

Yin, Z (author). “Assessment of Parkinson's Disease Severity from Videos using Deep Architectures.” 2020. Masters Thesis, Delft University of Technology. Accessed May 09, 2021. http://resolver.tudelft.nl/uuid:a0336a50-d169-45cb-abe7-097ba8d15084.

MLA Handbook (7th Edition):

Yin, Z (author). “Assessment of Parkinson's Disease Severity from Videos using Deep Architectures.” 2020. Web. 09 May 2021.

Vancouver:

Yin Z(. Assessment of Parkinson's Disease Severity from Videos using Deep Architectures. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 May 09]. Available from: http://resolver.tudelft.nl/uuid:a0336a50-d169-45cb-abe7-097ba8d15084.

Council of Science Editors:

Yin Z(. Assessment of Parkinson's Disease Severity from Videos using Deep Architectures. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:a0336a50-d169-45cb-abe7-097ba8d15084


University of Technology, Sydney

4. Zuo, Hua. Transfer learning in Takagi-Sugeno fuzzy models.

Degree: 2018, University of Technology, Sydney

 In classical data-driven machine learning methods, massive amounts of labeled data are required to build a high-performance prediction model. However, the amount of labeled data… (more)

Subjects/Keywords: Machine learning.; Heterogeneous transfer learning.; Fuzzy domain adaptation.; Heterogeneous domain adaptation method.

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Zuo, H. (2018). Transfer learning in Takagi-Sugeno fuzzy models. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/127891

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):

Zuo, Hua. “Transfer learning in Takagi-Sugeno fuzzy models.” 2018. Thesis, University of Technology, Sydney. Accessed May 09, 2021. http://hdl.handle.net/10453/127891.

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

MLA Handbook (7th Edition):

Zuo, Hua. “Transfer learning in Takagi-Sugeno fuzzy models.” 2018. Web. 09 May 2021.

Vancouver:

Zuo H. Transfer learning in Takagi-Sugeno fuzzy models. [Internet] [Thesis]. University of Technology, Sydney; 2018. [cited 2021 May 09]. Available from: http://hdl.handle.net/10453/127891.

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

Council of Science Editors:

Zuo H. Transfer learning in Takagi-Sugeno fuzzy models. [Thesis]. University of Technology, Sydney; 2018. Available from: http://hdl.handle.net/10453/127891

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


Boston University

5. Peng, Xingchao. Domain adaptive learning with disentangled features.

Degree: PhD, Computer Science, 2020, Boston University

 Recognizing visual information is crucial for many real artificial-intelligence-based applications, ranging from domestic robots to autonomous vehicles. However, the success of deep learning methods on… (more)

Subjects/Keywords: Computer science; Deep learning; Domain adaptation; Feature disentanglement; Transfer learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Peng, X. (2020). Domain adaptive learning with disentangled features. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/42065

Chicago Manual of Style (16th Edition):

Peng, Xingchao. “Domain adaptive learning with disentangled features.” 2020. Doctoral Dissertation, Boston University. Accessed May 09, 2021. http://hdl.handle.net/2144/42065.

MLA Handbook (7th Edition):

Peng, Xingchao. “Domain adaptive learning with disentangled features.” 2020. Web. 09 May 2021.

Vancouver:

Peng X. Domain adaptive learning with disentangled features. [Internet] [Doctoral dissertation]. Boston University; 2020. [cited 2021 May 09]. Available from: http://hdl.handle.net/2144/42065.

Council of Science Editors:

Peng X. Domain adaptive learning with disentangled features. [Doctoral Dissertation]. Boston University; 2020. Available from: http://hdl.handle.net/2144/42065


Kansas State University

6. Li, Hongmin. Domain adaptation approaches for classifying social media crisis data.

Degree: PhD, Department of Computer Science, 2020, Kansas State University

 Social media platforms such as Twitter provide valuable information for aiding first response during emergency events. Machine learning could be used to build automatic tools… (more)

Subjects/Keywords: Domain Adaptation; transfer learning; text classification; social media; crisis tweets classification

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Li, H. (2020). Domain adaptation approaches for classifying social media crisis data. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/40987

Chicago Manual of Style (16th Edition):

Li, Hongmin. “Domain adaptation approaches for classifying social media crisis data.” 2020. Doctoral Dissertation, Kansas State University. Accessed May 09, 2021. http://hdl.handle.net/2097/40987.

MLA Handbook (7th Edition):

Li, Hongmin. “Domain adaptation approaches for classifying social media crisis data.” 2020. Web. 09 May 2021.

Vancouver:

Li H. Domain adaptation approaches for classifying social media crisis data. [Internet] [Doctoral dissertation]. Kansas State University; 2020. [cited 2021 May 09]. Available from: http://hdl.handle.net/2097/40987.

Council of Science Editors:

Li H. Domain adaptation approaches for classifying social media crisis data. [Doctoral Dissertation]. Kansas State University; 2020. Available from: http://hdl.handle.net/2097/40987

7. Ayalew, Tewodros Wondifraw. Unsupervised Domain Adaptation for Object Counting.

Degree: 2020, University of Saskatchewan

 Supervised learning is a common approach for counting objects in images, but for counting small, densely located objects, the required image annotations are burdensome to… (more)

Subjects/Keywords: Object counting; Unsupervised Domain Adaptation; Density-based counting; Deep learning; Transfer learning; indoor-to-outdoor domain adaptation; species-to-species domain adaptation; synthetic-to-real domain adaptation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ayalew, T. W. (2020). Unsupervised Domain Adaptation for Object Counting. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/13235

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):

Ayalew, Tewodros Wondifraw. “Unsupervised Domain Adaptation for Object Counting.” 2020. Thesis, University of Saskatchewan. Accessed May 09, 2021. http://hdl.handle.net/10388/13235.

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

MLA Handbook (7th Edition):

Ayalew, Tewodros Wondifraw. “Unsupervised Domain Adaptation for Object Counting.” 2020. Web. 09 May 2021.

Vancouver:

Ayalew TW. Unsupervised Domain Adaptation for Object Counting. [Internet] [Thesis]. University of Saskatchewan; 2020. [cited 2021 May 09]. Available from: http://hdl.handle.net/10388/13235.

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

Council of Science Editors:

Ayalew TW. Unsupervised Domain Adaptation for Object Counting. [Thesis]. University of Saskatchewan; 2020. Available from: http://hdl.handle.net/10388/13235

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


Linköping University

8. Pettersson, Harald. Sentiment analysis and transfer learning using recurrent neural networks : an investigation of the power of transfer learning.

Degree: Human-Centered systems, 2019, Linköping University

  In the field of data mining, transfer learning is the method of transferring knowledge from one domain into another. Using reviews from prisjakt.se, a… (more)

Subjects/Keywords: Machine Learning; Neural Networks; Transfer Learning; Domain Adaption; Sentiment Analysis; Computer Engineering; Datorteknik

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Pettersson, H. (2019). Sentiment analysis and transfer learning using recurrent neural networks : an investigation of the power of transfer learning. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-161348

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):

Pettersson, Harald. “Sentiment analysis and transfer learning using recurrent neural networks : an investigation of the power of transfer learning.” 2019. Thesis, Linköping University. Accessed May 09, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-161348.

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

MLA Handbook (7th Edition):

Pettersson, Harald. “Sentiment analysis and transfer learning using recurrent neural networks : an investigation of the power of transfer learning.” 2019. Web. 09 May 2021.

Vancouver:

Pettersson H. Sentiment analysis and transfer learning using recurrent neural networks : an investigation of the power of transfer learning. [Internet] [Thesis]. Linköping University; 2019. [cited 2021 May 09]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-161348.

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

Council of Science Editors:

Pettersson H. Sentiment analysis and transfer learning using recurrent neural networks : an investigation of the power of transfer learning. [Thesis]. Linköping University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-161348

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


De Montfort University

9. Shell, Jethro. Fuzzy transfer learning.

Degree: PhD, 2013, De Montfort University

 The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world… (more)

Subjects/Keywords: 006.3; Transfer Learning; fuzzy logic; adaptive; online learning; context; domain adaptation; intelligent environments; sensor networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Shell, J. (2013). Fuzzy transfer learning. (Doctoral Dissertation). De Montfort University. Retrieved from http://hdl.handle.net/2086/8842

Chicago Manual of Style (16th Edition):

Shell, Jethro. “Fuzzy transfer learning.” 2013. Doctoral Dissertation, De Montfort University. Accessed May 09, 2021. http://hdl.handle.net/2086/8842.

MLA Handbook (7th Edition):

Shell, Jethro. “Fuzzy transfer learning.” 2013. Web. 09 May 2021.

Vancouver:

Shell J. Fuzzy transfer learning. [Internet] [Doctoral dissertation]. De Montfort University; 2013. [cited 2021 May 09]. Available from: http://hdl.handle.net/2086/8842.

Council of Science Editors:

Shell J. Fuzzy transfer learning. [Doctoral Dissertation]. De Montfort University; 2013. Available from: http://hdl.handle.net/2086/8842


Delft University of Technology

10. Datta, Leonid (author). Weight Swapping: A new method for Supervised Domain Adaptation in Computer Vision using Discrete Optimization.

Degree: 2020, Delft University of Technology

 Training Convolutional Neural Network (CNN) models is difficult when there is a lack of labeled training data and no unlabeled data is available. A popular… (more)

Subjects/Keywords: deep learning; computer vision; domain adaptation; Convolutional Neural Networks (CNNs); transfer learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Datta, L. (. (2020). Weight Swapping: A new method for Supervised Domain Adaptation in Computer Vision using Discrete Optimization. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:63511523-64d1-4b5e-ab66-26a5d0d61bd4

Chicago Manual of Style (16th Edition):

Datta, Leonid (author). “Weight Swapping: A new method for Supervised Domain Adaptation in Computer Vision using Discrete Optimization.” 2020. Masters Thesis, Delft University of Technology. Accessed May 09, 2021. http://resolver.tudelft.nl/uuid:63511523-64d1-4b5e-ab66-26a5d0d61bd4.

MLA Handbook (7th Edition):

Datta, Leonid (author). “Weight Swapping: A new method for Supervised Domain Adaptation in Computer Vision using Discrete Optimization.” 2020. Web. 09 May 2021.

Vancouver:

Datta L(. Weight Swapping: A new method for Supervised Domain Adaptation in Computer Vision using Discrete Optimization. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 May 09]. Available from: http://resolver.tudelft.nl/uuid:63511523-64d1-4b5e-ab66-26a5d0d61bd4.

Council of Science Editors:

Datta L(. Weight Swapping: A new method for Supervised Domain Adaptation in Computer Vision using Discrete Optimization. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:63511523-64d1-4b5e-ab66-26a5d0d61bd4


Purdue University

11. Das, Debasmit. On Transfer Learning Techniques for Machine Learning.

Degree: Electrical and Computer Engineering, 2020, Purdue University

 <pre><pre> Recent progress in machine learning has been mainly due to the availability of large amounts of annotated data used for training complex models with… (more)

Subjects/Keywords: Knowledge Representation and Machine Learning; Transfer learning; Computer Vision; Machine Learning; Domain Adaptation; Few-shot Learning; Zero-shot Learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Das, D. (2020). On Transfer Learning Techniques for Machine Learning. (Thesis). Purdue University. Retrieved from http://hdl.handle.net/10.25394/pgs.12221597.v1

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):

Das, Debasmit. “On Transfer Learning Techniques for Machine Learning.” 2020. Thesis, Purdue University. Accessed May 09, 2021. http://hdl.handle.net/10.25394/pgs.12221597.v1.

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

MLA Handbook (7th Edition):

Das, Debasmit. “On Transfer Learning Techniques for Machine Learning.” 2020. Web. 09 May 2021.

Vancouver:

Das D. On Transfer Learning Techniques for Machine Learning. [Internet] [Thesis]. Purdue University; 2020. [cited 2021 May 09]. Available from: http://hdl.handle.net/10.25394/pgs.12221597.v1.

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

Council of Science Editors:

Das D. On Transfer Learning Techniques for Machine Learning. [Thesis]. Purdue University; 2020. Available from: http://hdl.handle.net/10.25394/pgs.12221597.v1

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


University of Oxford

12. Wulfmeier, Markus. Efficient supervision for robot learning via imitation, simulation, and adaptation.

Degree: PhD, 2018, University of Oxford

 In order to enable more widespread application of robots, we are required to reduce the human effort for the introduction of existing robotic platforms to… (more)

Subjects/Keywords: 006.3; Machine learning; Robotics; Domain Adaptation; Imitation Learning; Inverse Reinforcement Learning; Mobile Robotics; Transfer Learning; Autonomous Driving

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Wulfmeier, M. (2018). Efficient supervision for robot learning via imitation, simulation, and adaptation. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:2b5eeb55-639a-40ae-83b7-bd01fc8fd6cc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757819

Chicago Manual of Style (16th Edition):

Wulfmeier, Markus. “Efficient supervision for robot learning via imitation, simulation, and adaptation.” 2018. Doctoral Dissertation, University of Oxford. Accessed May 09, 2021. http://ora.ox.ac.uk/objects/uuid:2b5eeb55-639a-40ae-83b7-bd01fc8fd6cc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757819.

MLA Handbook (7th Edition):

Wulfmeier, Markus. “Efficient supervision for robot learning via imitation, simulation, and adaptation.” 2018. Web. 09 May 2021.

Vancouver:

Wulfmeier M. Efficient supervision for robot learning via imitation, simulation, and adaptation. [Internet] [Doctoral dissertation]. University of Oxford; 2018. [cited 2021 May 09]. Available from: http://ora.ox.ac.uk/objects/uuid:2b5eeb55-639a-40ae-83b7-bd01fc8fd6cc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757819.

Council of Science Editors:

Wulfmeier M. Efficient supervision for robot learning via imitation, simulation, and adaptation. [Doctoral Dissertation]. University of Oxford; 2018. Available from: http://ora.ox.ac.uk/objects/uuid:2b5eeb55-639a-40ae-83b7-bd01fc8fd6cc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757819


University of Pennsylvania

13. Rostami, Mohammad. Learning Transferable Knowledge Through Embedding Spaces.

Degree: 2019, University of Pennsylvania

 The unprecedented processing demand, posed by the explosion of big data, challenges researchers to design efficient and adaptive machine learning algorithms that do not require… (more)

Subjects/Keywords: Domain Adaptation; Embedding Space; Lifelong Machine Learning; Multitask Learning; Transfer Learning; Zero-shot Learning; Computer Sciences; Electrical and Electronics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Rostami, M. (2019). Learning Transferable Knowledge Through Embedding Spaces. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/3525

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):

Rostami, Mohammad. “Learning Transferable Knowledge Through Embedding Spaces.” 2019. Thesis, University of Pennsylvania. Accessed May 09, 2021. https://repository.upenn.edu/edissertations/3525.

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

MLA Handbook (7th Edition):

Rostami, Mohammad. “Learning Transferable Knowledge Through Embedding Spaces.” 2019. Web. 09 May 2021.

Vancouver:

Rostami M. Learning Transferable Knowledge Through Embedding Spaces. [Internet] [Thesis]. University of Pennsylvania; 2019. [cited 2021 May 09]. Available from: https://repository.upenn.edu/edissertations/3525.

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

Council of Science Editors:

Rostami M. Learning Transferable Knowledge Through Embedding Spaces. [Thesis]. University of Pennsylvania; 2019. Available from: https://repository.upenn.edu/edissertations/3525

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


Penn State University

14. Sawant, Neela Kamlakar. Statistical Modeling of Image Semantics from Imperfectly Labeled Data Sets.

Degree: 2013, Penn State University

 Computer vision is an integral aspect of cognitive computing with diverse applications in medical diagnostics and health-care, communication, transportation, entertainment, and data management. It enables… (more)

Subjects/Keywords: statistical modeling; image annotation; ARTEMIS; instance-weighting; mixture models; transfer learning; domain adaptation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Sawant, N. K. (2013). Statistical Modeling of Image Semantics from Imperfectly Labeled Data Sets. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/18875

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):

Sawant, Neela Kamlakar. “Statistical Modeling of Image Semantics from Imperfectly Labeled Data Sets.” 2013. Thesis, Penn State University. Accessed May 09, 2021. https://submit-etda.libraries.psu.edu/catalog/18875.

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

MLA Handbook (7th Edition):

Sawant, Neela Kamlakar. “Statistical Modeling of Image Semantics from Imperfectly Labeled Data Sets.” 2013. Web. 09 May 2021.

Vancouver:

Sawant NK. Statistical Modeling of Image Semantics from Imperfectly Labeled Data Sets. [Internet] [Thesis]. Penn State University; 2013. [cited 2021 May 09]. Available from: https://submit-etda.libraries.psu.edu/catalog/18875.

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

Council of Science Editors:

Sawant NK. Statistical Modeling of Image Semantics from Imperfectly Labeled Data Sets. [Thesis]. Penn State University; 2013. Available from: https://submit-etda.libraries.psu.edu/catalog/18875

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


University of Cincinnati

15. Yang, Qibo. A Transfer Learning Methodology of Domain Generalization for Prognostics and Health Management.

Degree: PhD, Engineering and Applied Science: Mechanical Engineering, 2020, University of Cincinnati

 In recent years, prognostics and health management (PHM) using machine learning methods have been widely developed for industrial applications. However, the traditional methods assume that… (more)

Subjects/Keywords: Mechanical Engineering; Prognostics and health management; Transfer learning; Domain generalization; Maximum mean discrepancy; Neural networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Yang, Q. (2020). A Transfer Learning Methodology of Domain Generalization for Prognostics and Health Management. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613749034966366

Chicago Manual of Style (16th Edition):

Yang, Qibo. “A Transfer Learning Methodology of Domain Generalization for Prognostics and Health Management.” 2020. Doctoral Dissertation, University of Cincinnati. Accessed May 09, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613749034966366.

MLA Handbook (7th Edition):

Yang, Qibo. “A Transfer Learning Methodology of Domain Generalization for Prognostics and Health Management.” 2020. Web. 09 May 2021.

Vancouver:

Yang Q. A Transfer Learning Methodology of Domain Generalization for Prognostics and Health Management. [Internet] [Doctoral dissertation]. University of Cincinnati; 2020. [cited 2021 May 09]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613749034966366.

Council of Science Editors:

Yang Q. A Transfer Learning Methodology of Domain Generalization for Prognostics and Health Management. [Doctoral Dissertation]. University of Cincinnati; 2020. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613749034966366


University of Maryland

16. Zheng, Jingjing. Domain Transfer Learning for Object and Action Recognition.

Degree: Electrical Engineering, 2015, University of Maryland

 Visual recognition has always been a fundamental problem in computer vision. Its task is to learn visual categories using labeled training data and then identify… (more)

Subjects/Keywords: Electrical engineering; Computer science; Action recognition; Dictionary learning; Domain transfer; Object recongition

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Zheng, J. (2015). Domain Transfer Learning for Object and Action Recognition. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/17349

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):

Zheng, Jingjing. “Domain Transfer Learning for Object and Action Recognition.” 2015. Thesis, University of Maryland. Accessed May 09, 2021. http://hdl.handle.net/1903/17349.

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

MLA Handbook (7th Edition):

Zheng, Jingjing. “Domain Transfer Learning for Object and Action Recognition.” 2015. Web. 09 May 2021.

Vancouver:

Zheng J. Domain Transfer Learning for Object and Action Recognition. [Internet] [Thesis]. University of Maryland; 2015. [cited 2021 May 09]. Available from: http://hdl.handle.net/1903/17349.

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

Council of Science Editors:

Zheng J. Domain Transfer Learning for Object and Action Recognition. [Thesis]. University of Maryland; 2015. Available from: http://hdl.handle.net/1903/17349

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

17. Peyrache, Jean-Philippe. Nouvelles approches itératives avec garanties théoriques pour l'adaptation de domaine non supervisée : New iterative approaches with theoretical guarantees for unsupervised domain adaptation.

Degree: Docteur es, Informatique, 2014, Saint-Etienne

Ces dernières années, l’intérêt pour l’apprentissage automatique n’a cessé d’augmenter dans des domaines aussi variés que la reconnaissance d’images ou l’analyse de données médicales. Cependant,… (more)

Subjects/Keywords: Adaptation de domaine; Apprentissage automatique; Apprentissage semi-supervisé; Apprentissage par transfert; Domain adaptation; Machine learning; Semi-supervised learning; Learning by transfer

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Peyrache, J. (2014). Nouvelles approches itératives avec garanties théoriques pour l'adaptation de domaine non supervisée : New iterative approaches with theoretical guarantees for unsupervised domain adaptation. (Doctoral Dissertation). Saint-Etienne. Retrieved from http://www.theses.fr/2014STET4023

Chicago Manual of Style (16th Edition):

Peyrache, Jean-Philippe. “Nouvelles approches itératives avec garanties théoriques pour l'adaptation de domaine non supervisée : New iterative approaches with theoretical guarantees for unsupervised domain adaptation.” 2014. Doctoral Dissertation, Saint-Etienne. Accessed May 09, 2021. http://www.theses.fr/2014STET4023.

MLA Handbook (7th Edition):

Peyrache, Jean-Philippe. “Nouvelles approches itératives avec garanties théoriques pour l'adaptation de domaine non supervisée : New iterative approaches with theoretical guarantees for unsupervised domain adaptation.” 2014. Web. 09 May 2021.

Vancouver:

Peyrache J. Nouvelles approches itératives avec garanties théoriques pour l'adaptation de domaine non supervisée : New iterative approaches with theoretical guarantees for unsupervised domain adaptation. [Internet] [Doctoral dissertation]. Saint-Etienne; 2014. [cited 2021 May 09]. Available from: http://www.theses.fr/2014STET4023.

Council of Science Editors:

Peyrache J. Nouvelles approches itératives avec garanties théoriques pour l'adaptation de domaine non supervisée : New iterative approaches with theoretical guarantees for unsupervised domain adaptation. [Doctoral Dissertation]. Saint-Etienne; 2014. Available from: http://www.theses.fr/2014STET4023


Georgia Tech

18. Chen, Min-Hung. Bridging distributional discrepancy with temporal dynamics for video understanding.

Degree: PhD, Electrical and Computer Engineering, 2020, Georgia Tech

 Video has become one of the major media in our society, bringing considerable interests in the development of video analysis techniques for various applications. Temporal… (more)

Subjects/Keywords: Domain adaptation; Action recognition; Action segmentation; Self-supervised learning; Video understanding; Transfer learning; Unsupervised learning; Temporal dynamics; Domain discrepancy; Temporal variations; Multi-scale

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Chen, M. (2020). Bridging distributional discrepancy with temporal dynamics for video understanding. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63572

Chicago Manual of Style (16th Edition):

Chen, Min-Hung. “Bridging distributional discrepancy with temporal dynamics for video understanding.” 2020. Doctoral Dissertation, Georgia Tech. Accessed May 09, 2021. http://hdl.handle.net/1853/63572.

MLA Handbook (7th Edition):

Chen, Min-Hung. “Bridging distributional discrepancy with temporal dynamics for video understanding.” 2020. Web. 09 May 2021.

Vancouver:

Chen M. Bridging distributional discrepancy with temporal dynamics for video understanding. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 May 09]. Available from: http://hdl.handle.net/1853/63572.

Council of Science Editors:

Chen M. Bridging distributional discrepancy with temporal dynamics for video understanding. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63572


Halmstad University

19. Olsson, Anton. Domain Transfer for End-to-end Reinforcement Learning.

Degree: Information Technology, 2020, Halmstad University

  In this master thesis project a LiDAR-based, depth image-based and semantic segmentation image-based reinforcement learning agent is investigated and compared forlearning in simulation and… (more)

Subjects/Keywords: Reinforcement Learning; Domain Transfer; Deep Deterministic Policy Gradient; Reinforcement Learning in Real-time; Computer Sciences; Datavetenskap (datalogi); Computer Engineering; Datorteknik

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Olsson, A. (2020). Domain Transfer for End-to-end Reinforcement Learning. (Thesis). Halmstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43042

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):

Olsson, Anton. “Domain Transfer for End-to-end Reinforcement Learning.” 2020. Thesis, Halmstad University. Accessed May 09, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43042.

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

MLA Handbook (7th Edition):

Olsson, Anton. “Domain Transfer for End-to-end Reinforcement Learning.” 2020. Web. 09 May 2021.

Vancouver:

Olsson A. Domain Transfer for End-to-end Reinforcement Learning. [Internet] [Thesis]. Halmstad University; 2020. [cited 2021 May 09]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43042.

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

Council of Science Editors:

Olsson A. Domain Transfer for End-to-end Reinforcement Learning. [Thesis]. Halmstad University; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43042

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


Northeastern University

20. Shao, Ming. Efficient transfer feature learning and its applications on social media.

Degree: PhD, Department of Electrical and Computer Engineering, 2016, Northeastern University

 In the era of social media, more and more social characteristics are conveyed by multimedia, i.e., images, videos, audios, and webpages with rich media information.… (more)

Subjects/Keywords: domain adaptation; kinship verification; low-rank matrix analysis; transfer learning; Computer vision; Machine learning; Social media; Biometric identification; Mathematical models; Matrices

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Shao, M. (2016). Efficient transfer feature learning and its applications on social media. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20213068

Chicago Manual of Style (16th Edition):

Shao, Ming. “Efficient transfer feature learning and its applications on social media.” 2016. Doctoral Dissertation, Northeastern University. Accessed May 09, 2021. http://hdl.handle.net/2047/D20213068.

MLA Handbook (7th Edition):

Shao, Ming. “Efficient transfer feature learning and its applications on social media.” 2016. Web. 09 May 2021.

Vancouver:

Shao M. Efficient transfer feature learning and its applications on social media. [Internet] [Doctoral dissertation]. Northeastern University; 2016. [cited 2021 May 09]. Available from: http://hdl.handle.net/2047/D20213068.

Council of Science Editors:

Shao M. Efficient transfer feature learning and its applications on social media. [Doctoral Dissertation]. Northeastern University; 2016. Available from: http://hdl.handle.net/2047/D20213068


Carnegie Mellon University

21. Zhao, Han. Towards a Unified Framework for Learning and Reasoning.

Degree: Theses and Dissertations, 2021, Carnegie Mellon University

 AbstractThe success of supervised machine learning in recent years crucially hinges on the availability of large-scale and unbiased data, which is often time-consuming and expensive… (more)

Subjects/Keywords: Theoretical Computer Science; Machine Learning,; Artificial Intelligence; Deep Learning; Representation Learning; information theory; Learning Theory; Algorithmic Fairness; Domain Adaptation; Transfer Learning; Probabilistic Circuits

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Zhao, H. (2021). Towards a Unified Framework for Learning and Reasoning. (Thesis). Carnegie Mellon University. Retrieved from http://hdl.handle.net/10.1184/r1/14394497.v1

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):

Zhao, Han. “Towards a Unified Framework for Learning and Reasoning.” 2021. Thesis, Carnegie Mellon University. Accessed May 09, 2021. http://hdl.handle.net/10.1184/r1/14394497.v1.

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

MLA Handbook (7th Edition):

Zhao, Han. “Towards a Unified Framework for Learning and Reasoning.” 2021. Web. 09 May 2021.

Vancouver:

Zhao H. Towards a Unified Framework for Learning and Reasoning. [Internet] [Thesis]. Carnegie Mellon University; 2021. [cited 2021 May 09]. Available from: http://hdl.handle.net/10.1184/r1/14394497.v1.

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

Council of Science Editors:

Zhao H. Towards a Unified Framework for Learning and Reasoning. [Thesis]. Carnegie Mellon University; 2021. Available from: http://hdl.handle.net/10.1184/r1/14394497.v1

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


Carnegie Mellon University

22. Chu, Wen-Sheng. Automatic Analysis of Facial Actions: Learning from Transductive, Supervised and Unsupervised Frameworks.

Degree: 2017, Carnegie Mellon University

 Automatic analysis of facial actions (AFA) can reveal a person’s emotion, intention, and physical state, and make possible a wide range of applications. To enable… (more)

Subjects/Keywords: Automated measurement; facial expression analysis; facial action unit (AU) detection; transfer learning; domain adaptation; importance re-weighting

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Chu, W. (2017). Automatic Analysis of Facial Actions: Learning from Transductive, Supervised and Unsupervised Frameworks. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/929

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):

Chu, Wen-Sheng. “Automatic Analysis of Facial Actions: Learning from Transductive, Supervised and Unsupervised Frameworks.” 2017. Thesis, Carnegie Mellon University. Accessed May 09, 2021. http://repository.cmu.edu/dissertations/929.

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

MLA Handbook (7th Edition):

Chu, Wen-Sheng. “Automatic Analysis of Facial Actions: Learning from Transductive, Supervised and Unsupervised Frameworks.” 2017. Web. 09 May 2021.

Vancouver:

Chu W. Automatic Analysis of Facial Actions: Learning from Transductive, Supervised and Unsupervised Frameworks. [Internet] [Thesis]. Carnegie Mellon University; 2017. [cited 2021 May 09]. Available from: http://repository.cmu.edu/dissertations/929.

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

Council of Science Editors:

Chu W. Automatic Analysis of Facial Actions: Learning from Transductive, Supervised and Unsupervised Frameworks. [Thesis]. Carnegie Mellon University; 2017. Available from: http://repository.cmu.edu/dissertations/929

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

23. Kundu, Gourab. Domain adaptation with minimal training.

Degree: PhD, 0112, 2015, University of Illinois – Urbana-Champaign

 The performance of a machine learning model trained on labeled data of a (source) domain degrades severely when they are tested on a different (target)… (more)

Subjects/Keywords: Domain Adaptation; Transfer Learning; Named Entity Recognition

…1.2 The Need for Domain Adaptation Statistical learning based methods have been very… …this thesis. A reader familiar with the basic machine learning concepts and domain adaptation… …xiii Chapter 1 Introduction . . . . . . . . . . . . . . . . . 1.1 Statistical Learning in… …NLP . . . . . . . . . . . . . . 1.2 The Need for Domain Adaptation . . . . . . . . . . . 1.3… …2.2 Classification . . . . . . . . . . . . . . . . . . . . . . 2.3 Learning Protocols… 

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7 Sample image

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Kundu, G. (2015). Domain adaptation with minimal training. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/72928

Chicago Manual of Style (16th Edition):

Kundu, Gourab. “Domain adaptation with minimal training.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed May 09, 2021. http://hdl.handle.net/2142/72928.

MLA Handbook (7th Edition):

Kundu, Gourab. “Domain adaptation with minimal training.” 2015. Web. 09 May 2021.

Vancouver:

Kundu G. Domain adaptation with minimal training. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 May 09]. Available from: http://hdl.handle.net/2142/72928.

Council of Science Editors:

Kundu G. Domain adaptation with minimal training. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/72928

24. LI JUNNAN. LABEL EFFICIENT LEARNING BEYOND MANUAL ANNOTATIONS.

Degree: 2019, National University of Singapore

Subjects/Keywords: image classification; label noise learning; unsupervised learning; action recognition; transfer learning; domain adaptation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

JUNNAN, L. (2019). LABEL EFFICIENT LEARNING BEYOND MANUAL ANNOTATIONS. (Thesis). National University of Singapore. Retrieved from https://scholarbank.nus.edu.sg/handle/10635/162736

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):

JUNNAN, LI. “LABEL EFFICIENT LEARNING BEYOND MANUAL ANNOTATIONS.” 2019. Thesis, National University of Singapore. Accessed May 09, 2021. https://scholarbank.nus.edu.sg/handle/10635/162736.

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

MLA Handbook (7th Edition):

JUNNAN, LI. “LABEL EFFICIENT LEARNING BEYOND MANUAL ANNOTATIONS.” 2019. Web. 09 May 2021.

Vancouver:

JUNNAN L. LABEL EFFICIENT LEARNING BEYOND MANUAL ANNOTATIONS. [Internet] [Thesis]. National University of Singapore; 2019. [cited 2021 May 09]. Available from: https://scholarbank.nus.edu.sg/handle/10635/162736.

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

Council of Science Editors:

JUNNAN L. LABEL EFFICIENT LEARNING BEYOND MANUAL ANNOTATIONS. [Thesis]. National University of Singapore; 2019. Available from: https://scholarbank.nus.edu.sg/handle/10635/162736

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


Arizona State University

25. Eusebio, Jose Miguel Ang. Learning Transferable Data Representations Using Deep Generative Models.

Degree: Computer Science, 2018, Arizona State University

Subjects/Keywords: Computer science; Deep Learning; Domain Adaptation; Generative Models; Machine Learning; Transfer Learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Eusebio, J. M. A. (2018). Learning Transferable Data Representations Using Deep Generative Models. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/49287

Chicago Manual of Style (16th Edition):

Eusebio, Jose Miguel Ang. “Learning Transferable Data Representations Using Deep Generative Models.” 2018. Masters Thesis, Arizona State University. Accessed May 09, 2021. http://repository.asu.edu/items/49287.

MLA Handbook (7th Edition):

Eusebio, Jose Miguel Ang. “Learning Transferable Data Representations Using Deep Generative Models.” 2018. Web. 09 May 2021.

Vancouver:

Eusebio JMA. Learning Transferable Data Representations Using Deep Generative Models. [Internet] [Masters thesis]. Arizona State University; 2018. [cited 2021 May 09]. Available from: http://repository.asu.edu/items/49287.

Council of Science Editors:

Eusebio JMA. Learning Transferable Data Representations Using Deep Generative Models. [Masters Thesis]. Arizona State University; 2018. Available from: http://repository.asu.edu/items/49287


The Ohio State University

26. Johnson, Travis Steele. Integrative approaches to single cell RNA sequencing analysis.

Degree: PhD, Biomedical Sciences, 2020, The Ohio State University

 There are trillions of cells, which make up hundreds of different cell types, found in the human body. These cells make up not only tissues… (more)

Subjects/Keywords: Biomedical Research; Bioinformatics; RNA sequencing, RNA-seq, scRNA-seq, machine learning, transfer learning, domain adaptation, multitask learning, brain, neuron, glioblastoma, integrative analyses, bioinformatics, computational biology

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Johnson, T. S. (2020). Integrative approaches to single cell RNA sequencing analysis. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1586960661272666

Chicago Manual of Style (16th Edition):

Johnson, Travis Steele. “Integrative approaches to single cell RNA sequencing analysis.” 2020. Doctoral Dissertation, The Ohio State University. Accessed May 09, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586960661272666.

MLA Handbook (7th Edition):

Johnson, Travis Steele. “Integrative approaches to single cell RNA sequencing analysis.” 2020. Web. 09 May 2021.

Vancouver:

Johnson TS. Integrative approaches to single cell RNA sequencing analysis. [Internet] [Doctoral dissertation]. The Ohio State University; 2020. [cited 2021 May 09]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1586960661272666.

Council of Science Editors:

Johnson TS. Integrative approaches to single cell RNA sequencing analysis. [Doctoral Dissertation]. The Ohio State University; 2020. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1586960661272666

27. Magda Friedjungová. Pokročilé metody asymetrického heterogenního transfer learningu .

Degree: 2021, Czech University of Technology

 This dissertation thesis deals with the application of asymmetric heterogeneous transfer learning in scenarios where transfer of data is crucial due to the preservation of… (more)

Subjects/Keywords: Heterogeneous Asymmetric Transfer Learning; Missing Features; Data Imputation; Non-Generative Models; Generative Models; Latent Space; Domain Adaptation; Heterogeneous Asymmetric Transfer Learning; Missing Features; Data Imputation; Non-Generative Models; Generative Models; Latent Space; Domain Adaptation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Friedjungová, M. (2021). Pokročilé metody asymetrického heterogenního transfer learningu . (Thesis). Czech University of Technology. Retrieved from http://hdl.handle.net/10467/93701

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):

Friedjungová, Magda. “Pokročilé metody asymetrického heterogenního transfer learningu .” 2021. Thesis, Czech University of Technology. Accessed May 09, 2021. http://hdl.handle.net/10467/93701.

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

MLA Handbook (7th Edition):

Friedjungová, Magda. “Pokročilé metody asymetrického heterogenního transfer learningu .” 2021. Web. 09 May 2021.

Vancouver:

Friedjungová M. Pokročilé metody asymetrického heterogenního transfer learningu . [Internet] [Thesis]. Czech University of Technology; 2021. [cited 2021 May 09]. Available from: http://hdl.handle.net/10467/93701.

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

Council of Science Editors:

Friedjungová M. Pokročilé metody asymetrického heterogenního transfer learningu . [Thesis]. Czech University of Technology; 2021. Available from: http://hdl.handle.net/10467/93701

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


Erasmus University Rotterdam

28. Opbroek, Annegreet. Transfer Learning for Medical Image Segmentation.

Degree: 2018, Erasmus University Rotterdam

 textabstractMany medical-image-segmentation techniques are based on supervised learning, which assumes training data to be representative of the test data to segment. In practice however, training… (more)

Subjects/Keywords: Transfer Learning; Domain Adaptation; Medical Image Analysis; Segmentation; Machine Learning; Pattern Recognition

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Opbroek, A. (2018). Transfer Learning for Medical Image Segmentation. (Doctoral Dissertation). Erasmus University Rotterdam. Retrieved from http://hdl.handle.net/1765/105962

Chicago Manual of Style (16th Edition):

Opbroek, Annegreet. “Transfer Learning for Medical Image Segmentation.” 2018. Doctoral Dissertation, Erasmus University Rotterdam. Accessed May 09, 2021. http://hdl.handle.net/1765/105962.

MLA Handbook (7th Edition):

Opbroek, Annegreet. “Transfer Learning for Medical Image Segmentation.” 2018. Web. 09 May 2021.

Vancouver:

Opbroek A. Transfer Learning for Medical Image Segmentation. [Internet] [Doctoral dissertation]. Erasmus University Rotterdam; 2018. [cited 2021 May 09]. Available from: http://hdl.handle.net/1765/105962.

Council of Science Editors:

Opbroek A. Transfer Learning for Medical Image Segmentation. [Doctoral Dissertation]. Erasmus University Rotterdam; 2018. Available from: http://hdl.handle.net/1765/105962

29. ZHU JUN. DEVELOPMENT OF DEEP LEARNING METHODS FOR PROGNOSTICS AND HEALTH MANAGEMENT.

Degree: 2020, National University of Singapore

Subjects/Keywords: fault diagnosis, fault prognosis; deep learning, transfer learning, domain adaptation, roller bearing

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

JUN, Z. (2020). DEVELOPMENT OF DEEP LEARNING METHODS FOR PROGNOSTICS AND HEALTH MANAGEMENT. (Thesis). National University of Singapore. Retrieved from https://scholarbank.nus.edu.sg/handle/10635/185243

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):

JUN, ZHU. “DEVELOPMENT OF DEEP LEARNING METHODS FOR PROGNOSTICS AND HEALTH MANAGEMENT.” 2020. Thesis, National University of Singapore. Accessed May 09, 2021. https://scholarbank.nus.edu.sg/handle/10635/185243.

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

MLA Handbook (7th Edition):

JUN, ZHU. “DEVELOPMENT OF DEEP LEARNING METHODS FOR PROGNOSTICS AND HEALTH MANAGEMENT.” 2020. Web. 09 May 2021.

Vancouver:

JUN Z. DEVELOPMENT OF DEEP LEARNING METHODS FOR PROGNOSTICS AND HEALTH MANAGEMENT. [Internet] [Thesis]. National University of Singapore; 2020. [cited 2021 May 09]. Available from: https://scholarbank.nus.edu.sg/handle/10635/185243.

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

Council of Science Editors:

JUN Z. DEVELOPMENT OF DEEP LEARNING METHODS FOR PROGNOSTICS AND HEALTH MANAGEMENT. [Thesis]. National University of Singapore; 2020. Available from: https://scholarbank.nus.edu.sg/handle/10635/185243

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

30. Ivone Penque Matsuno Yugoshi. Mineração de opiniões baseada em aspectos para revisões de produtos e serviços.

Degree: 2018, University of São Paulo

A Mineração de Opiniões é um processo que tem por objetivo extrair as opiniões e suas polaridades de sentimentos expressas em textos em língua natural.… (more)

Subjects/Keywords: Análise de sentimentos; Aprendizado por transferência entre domínios; Aprendizado semissupervisionado; Extração de aspectos; Mineração de opiniões; Aspect extraction; Cross-domain transfer learning; Opinion mining; Semi-supervised learning; Sentiment analysis

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Yugoshi, I. P. M. (2018). Mineração de opiniões baseada em aspectos para revisões de produtos e serviços. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/55/55134/tde-17102018-112458/

Chicago Manual of Style (16th Edition):

Yugoshi, Ivone Penque Matsuno. “Mineração de opiniões baseada em aspectos para revisões de produtos e serviços.” 2018. Doctoral Dissertation, University of São Paulo. Accessed May 09, 2021. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-17102018-112458/.

MLA Handbook (7th Edition):

Yugoshi, Ivone Penque Matsuno. “Mineração de opiniões baseada em aspectos para revisões de produtos e serviços.” 2018. Web. 09 May 2021.

Vancouver:

Yugoshi IPM. Mineração de opiniões baseada em aspectos para revisões de produtos e serviços. [Internet] [Doctoral dissertation]. University of São Paulo; 2018. [cited 2021 May 09]. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-17102018-112458/.

Council of Science Editors:

Yugoshi IPM. Mineração de opiniões baseada em aspectos para revisões de produtos e serviços. [Doctoral Dissertation]. University of São Paulo; 2018. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-17102018-112458/

[1] [2]

.