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You searched for subject:(multi task learning). Showing records 1 – 30 of 79 total matches.

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Arizona State University

1. Zhou, Jiayu. Multi-Task Learning and Its Applications to Biomedical Informatics.

Degree: PhD, Computer Science, 2014, Arizona State University

 In many fields one needs to build predictive models for a set of related machine learning tasks, such as information retrieval, computer vision and biomedical… (more)

Subjects/Keywords: Computer science; multi-task learning

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APA (6th Edition):

Zhou, J. (2014). Multi-Task Learning and Its Applications to Biomedical Informatics. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/25176

Chicago Manual of Style (16th Edition):

Zhou, Jiayu. “Multi-Task Learning and Its Applications to Biomedical Informatics.” 2014. Doctoral Dissertation, Arizona State University. Accessed March 09, 2021. http://repository.asu.edu/items/25176.

MLA Handbook (7th Edition):

Zhou, Jiayu. “Multi-Task Learning and Its Applications to Biomedical Informatics.” 2014. Web. 09 Mar 2021.

Vancouver:

Zhou J. Multi-Task Learning and Its Applications to Biomedical Informatics. [Internet] [Doctoral dissertation]. Arizona State University; 2014. [cited 2021 Mar 09]. Available from: http://repository.asu.edu/items/25176.

Council of Science Editors:

Zhou J. Multi-Task Learning and Its Applications to Biomedical Informatics. [Doctoral Dissertation]. Arizona State University; 2014. Available from: http://repository.asu.edu/items/25176


Tampere University

2. Senhaji, Ali. Incremental Multi-Domain Learning with Domain-Specific Early Exits .

Degree: 2020, Tampere University

 Deep learning architectures can achieve state-of-the-art results in several computer vision tasks. However, these methods are highly specialized, i.e., for every task from a new… (more)

Subjects/Keywords: multi-domain learning ; early exits ; domain adaptation ; multi-task learning

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APA (6th Edition):

Senhaji, A. (2020). Incremental Multi-Domain Learning with Domain-Specific Early Exits . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/121513

Chicago Manual of Style (16th Edition):

Senhaji, Ali. “Incremental Multi-Domain Learning with Domain-Specific Early Exits .” 2020. Masters Thesis, Tampere University. Accessed March 09, 2021. https://trepo.tuni.fi/handle/10024/121513.

MLA Handbook (7th Edition):

Senhaji, Ali. “Incremental Multi-Domain Learning with Domain-Specific Early Exits .” 2020. Web. 09 Mar 2021.

Vancouver:

Senhaji A. Incremental Multi-Domain Learning with Domain-Specific Early Exits . [Internet] [Masters thesis]. Tampere University; 2020. [cited 2021 Mar 09]. Available from: https://trepo.tuni.fi/handle/10024/121513.

Council of Science Editors:

Senhaji A. Incremental Multi-Domain Learning with Domain-Specific Early Exits . [Masters Thesis]. Tampere University; 2020. Available from: https://trepo.tuni.fi/handle/10024/121513


University of Minnesota

3. Karpatne, Anuj. Predictive Learning with Heterogeneity in Populations.

Degree: PhD, Computer Science, 2017, University of Minnesota

 Predictive learning forms the backbone of several data-driven systems powering scientific as well as commercial applications, e.g., filtering spam messages, detecting faces in images, forecasting… (more)

Subjects/Keywords: data mining; ensemble learning; machine learning; multi-modality; multi-task learning; population heterogeneity

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APA (6th Edition):

Karpatne, A. (2017). Predictive Learning with Heterogeneity in Populations. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/192667

Chicago Manual of Style (16th Edition):

Karpatne, Anuj. “Predictive Learning with Heterogeneity in Populations.” 2017. Doctoral Dissertation, University of Minnesota. Accessed March 09, 2021. http://hdl.handle.net/11299/192667.

MLA Handbook (7th Edition):

Karpatne, Anuj. “Predictive Learning with Heterogeneity in Populations.” 2017. Web. 09 Mar 2021.

Vancouver:

Karpatne A. Predictive Learning with Heterogeneity in Populations. [Internet] [Doctoral dissertation]. University of Minnesota; 2017. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/11299/192667.

Council of Science Editors:

Karpatne A. Predictive Learning with Heterogeneity in Populations. [Doctoral Dissertation]. University of Minnesota; 2017. Available from: http://hdl.handle.net/11299/192667


AUT University

4. Fan, Liu. Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning .

Degree: 2011, AUT University

Multi-Task Learning (MTL), as opposed to Single Task Learning (STL), has become a hot topic in machine learning research. For many real world problems in… (more)

Subjects/Keywords: Multi-task Learning; Knowledge Transfer; Correlated multi-task learning; Minimum Enclosing Ball; Machine Learning; Knowledge Sharing; Learner Independence

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APA (6th Edition):

Fan, L. (2011). Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning . (Thesis). AUT University. Retrieved from http://hdl.handle.net/10292/1120

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):

Fan, Liu. “Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning .” 2011. Thesis, AUT University. Accessed March 09, 2021. http://hdl.handle.net/10292/1120.

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

MLA Handbook (7th Edition):

Fan, Liu. “Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning .” 2011. Web. 09 Mar 2021.

Vancouver:

Fan L. Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning . [Internet] [Thesis]. AUT University; 2011. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10292/1120.

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

Council of Science Editors:

Fan L. Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning . [Thesis]. AUT University; 2011. Available from: http://hdl.handle.net/10292/1120

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

5. Moura, Simon. Apprentissage multi-cibles : théorie et applications : Multi-output learning : theory and applications.

Degree: Docteur es, Informatique, 2018, Université Grenoble Alpes (ComUE)

Cette thèse traite du problème de l'apprentissage automatique supervisé dans le cas ou l'on considère plusieurs sorties, potentiellement de différent types. Nous proposons d'explorer trois… (more)

Subjects/Keywords: Apprentissage multi-Cibles; Apprentissage statistique; Apprentissage multi-Label; Multi-Output learning; Multi-Task learning; Statisticial learning; 004

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APA (6th Edition):

Moura, S. (2018). Apprentissage multi-cibles : théorie et applications : Multi-output learning : theory and applications. (Doctoral Dissertation). Université Grenoble Alpes (ComUE). Retrieved from http://www.theses.fr/2018GREAM085

Chicago Manual of Style (16th Edition):

Moura, Simon. “Apprentissage multi-cibles : théorie et applications : Multi-output learning : theory and applications.” 2018. Doctoral Dissertation, Université Grenoble Alpes (ComUE). Accessed March 09, 2021. http://www.theses.fr/2018GREAM085.

MLA Handbook (7th Edition):

Moura, Simon. “Apprentissage multi-cibles : théorie et applications : Multi-output learning : theory and applications.” 2018. Web. 09 Mar 2021.

Vancouver:

Moura S. Apprentissage multi-cibles : théorie et applications : Multi-output learning : theory and applications. [Internet] [Doctoral dissertation]. Université Grenoble Alpes (ComUE); 2018. [cited 2021 Mar 09]. Available from: http://www.theses.fr/2018GREAM085.

Council of Science Editors:

Moura S. Apprentissage multi-cibles : théorie et applications : Multi-output learning : theory and applications. [Doctoral Dissertation]. Université Grenoble Alpes (ComUE); 2018. Available from: http://www.theses.fr/2018GREAM085

6. Faddoul, Jean-Baptiste. Méthodes d’ensembles pour l’apprentissage multi-tâche avec des tâches hétérogènes et sans restrictions : Ensemble Methods to Learn Multiple Heterogenous Tasks without Restrictions.

Degree: Docteur es, Informatique, 2012, Lille 3

Apprendre des tâches simultanément peut améliorer la performance de prédiction par rapport à l'apprentissage de ces tâches de manière indépendante. Dans cette thèse, nous considérons… (more)

Subjects/Keywords: Apprentissage automatique; Boosting (algorithmes); Fonctionnement multitâche; Machine Learning; Multi-Task Learning

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APA (6th Edition):

Faddoul, J. (2012). Méthodes d’ensembles pour l’apprentissage multi-tâche avec des tâches hétérogènes et sans restrictions : Ensemble Methods to Learn Multiple Heterogenous Tasks without Restrictions. (Doctoral Dissertation). Lille 3. Retrieved from http://www.theses.fr/2012LIL30059

Chicago Manual of Style (16th Edition):

Faddoul, Jean-Baptiste. “Méthodes d’ensembles pour l’apprentissage multi-tâche avec des tâches hétérogènes et sans restrictions : Ensemble Methods to Learn Multiple Heterogenous Tasks without Restrictions.” 2012. Doctoral Dissertation, Lille 3. Accessed March 09, 2021. http://www.theses.fr/2012LIL30059.

MLA Handbook (7th Edition):

Faddoul, Jean-Baptiste. “Méthodes d’ensembles pour l’apprentissage multi-tâche avec des tâches hétérogènes et sans restrictions : Ensemble Methods to Learn Multiple Heterogenous Tasks without Restrictions.” 2012. Web. 09 Mar 2021.

Vancouver:

Faddoul J. Méthodes d’ensembles pour l’apprentissage multi-tâche avec des tâches hétérogènes et sans restrictions : Ensemble Methods to Learn Multiple Heterogenous Tasks without Restrictions. [Internet] [Doctoral dissertation]. Lille 3; 2012. [cited 2021 Mar 09]. Available from: http://www.theses.fr/2012LIL30059.

Council of Science Editors:

Faddoul J. Méthodes d’ensembles pour l’apprentissage multi-tâche avec des tâches hétérogènes et sans restrictions : Ensemble Methods to Learn Multiple Heterogenous Tasks without Restrictions. [Doctoral Dissertation]. Lille 3; 2012. Available from: http://www.theses.fr/2012LIL30059


University of Cambridge

7. Bruinsma, Wessel. The Generalised Gaussian Process Convolution Model.

Degree: MPhil, 2016, University of Cambridge

 This thesis formulates the Generalised Gaussian Process Convolution Model (GGPCM), which is a generalisation of the Gaussian Process Convolution Model presented by Tobar et al.… (more)

Subjects/Keywords: machine learning; Gaussian process; kernel; nonparametric kernel; multi-task learning

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APA (6th Edition):

Bruinsma, W. (2016). The Generalised Gaussian Process Convolution Model. (Masters Thesis). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/273357

Chicago Manual of Style (16th Edition):

Bruinsma, Wessel. “The Generalised Gaussian Process Convolution Model.” 2016. Masters Thesis, University of Cambridge. Accessed March 09, 2021. https://www.repository.cam.ac.uk/handle/1810/273357.

MLA Handbook (7th Edition):

Bruinsma, Wessel. “The Generalised Gaussian Process Convolution Model.” 2016. Web. 09 Mar 2021.

Vancouver:

Bruinsma W. The Generalised Gaussian Process Convolution Model. [Internet] [Masters thesis]. University of Cambridge; 2016. [cited 2021 Mar 09]. Available from: https://www.repository.cam.ac.uk/handle/1810/273357.

Council of Science Editors:

Bruinsma W. The Generalised Gaussian Process Convolution Model. [Masters Thesis]. University of Cambridge; 2016. Available from: https://www.repository.cam.ac.uk/handle/1810/273357


Delft University of Technology

8. Kaniouras, Pantelis (author). Road Detection from Remote Sensing Imagery.

Degree: 2020, Delft University of Technology

Road network maps facilitate a great number of applications in our everyday life. However, their automatic creation is a difficult task, and so far, published… (more)

Subjects/Keywords: multi-task learning; deep learning; road detection; Convolutional Neural Network

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APA (6th Edition):

Kaniouras, P. (. (2020). Road Detection from Remote Sensing Imagery. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:21fc20a8-455d-4583-9698-4fea04516f03

Chicago Manual of Style (16th Edition):

Kaniouras, Pantelis (author). “Road Detection from Remote Sensing Imagery.” 2020. Masters Thesis, Delft University of Technology. Accessed March 09, 2021. http://resolver.tudelft.nl/uuid:21fc20a8-455d-4583-9698-4fea04516f03.

MLA Handbook (7th Edition):

Kaniouras, Pantelis (author). “Road Detection from Remote Sensing Imagery.” 2020. Web. 09 Mar 2021.

Vancouver:

Kaniouras P(. Road Detection from Remote Sensing Imagery. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Mar 09]. Available from: http://resolver.tudelft.nl/uuid:21fc20a8-455d-4583-9698-4fea04516f03.

Council of Science Editors:

Kaniouras P(. Road Detection from Remote Sensing Imagery. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:21fc20a8-455d-4583-9698-4fea04516f03


Tampere University

9. Khan, Amna. Comparison of machine learning approaches for classification of invoices .

Degree: 2020, Tampere University

 Machine learning has become one of the leading sciences governing modern world. Various disciplines specifically neural networks have recently gained a lot of attention due… (more)

Subjects/Keywords: Machine Learning ; Invoice prediction ; Neural Networks ; Multi-task learning ; Continual Learning ; Deep Learning in Finance

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APA (6th Edition):

Khan, A. (2020). Comparison of machine learning approaches for classification of invoices . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/120493

Chicago Manual of Style (16th Edition):

Khan, Amna. “Comparison of machine learning approaches for classification of invoices .” 2020. Masters Thesis, Tampere University. Accessed March 09, 2021. https://trepo.tuni.fi/handle/10024/120493.

MLA Handbook (7th Edition):

Khan, Amna. “Comparison of machine learning approaches for classification of invoices .” 2020. Web. 09 Mar 2021.

Vancouver:

Khan A. Comparison of machine learning approaches for classification of invoices . [Internet] [Masters thesis]. Tampere University; 2020. [cited 2021 Mar 09]. Available from: https://trepo.tuni.fi/handle/10024/120493.

Council of Science Editors:

Khan A. Comparison of machine learning approaches for classification of invoices . [Masters Thesis]. Tampere University; 2020. Available from: https://trepo.tuni.fi/handle/10024/120493


University of Kansas

10. Li, Xiaoli. Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics.

Degree: PhD, Electrical Engineering & Computer Science, 2018, University of Kansas

 Aiming to achieve the learning capabilities possessed by intelligent beings, especially human, researchers in machine learning field have the long-standing tradition of bor- rowing ideas… (more)

Subjects/Keywords: Computer science; Bayesian Nonparametrics; Constructivism Learning; Multi-task Learning; Transparent Machine Learning

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APA (6th Edition):

Li, X. (2018). Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics. (Doctoral Dissertation). University of Kansas. Retrieved from http://hdl.handle.net/1808/27594

Chicago Manual of Style (16th Edition):

Li, Xiaoli. “Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics.” 2018. Doctoral Dissertation, University of Kansas. Accessed March 09, 2021. http://hdl.handle.net/1808/27594.

MLA Handbook (7th Edition):

Li, Xiaoli. “Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics.” 2018. Web. 09 Mar 2021.

Vancouver:

Li X. Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics. [Internet] [Doctoral dissertation]. University of Kansas; 2018. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/1808/27594.

Council of Science Editors:

Li X. Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics. [Doctoral Dissertation]. University of Kansas; 2018. Available from: http://hdl.handle.net/1808/27594


Rochester Institute of Technology

11. Sankaran, Prashant. Design and Simulation Analysis of Deep Learning Based Approaches and Multi-Attribute Algorithms for Warehouse Task Selection.

Degree: MS, Industrial and Systems Engineering, 2020, Rochester Institute of Technology

  With the growth and adoption of global supply chains and internet technologies, warehouse operations have become more demanding. Particularly, the number of orders being… (more)

Subjects/Keywords: Algorithm; Deep learning; Dynamic environment; Multi attribute task assignment; Simulation; Warehouse

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APA (6th Edition):

Sankaran, P. (2020). Design and Simulation Analysis of Deep Learning Based Approaches and Multi-Attribute Algorithms for Warehouse Task Selection. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10354

Chicago Manual of Style (16th Edition):

Sankaran, Prashant. “Design and Simulation Analysis of Deep Learning Based Approaches and Multi-Attribute Algorithms for Warehouse Task Selection.” 2020. Masters Thesis, Rochester Institute of Technology. Accessed March 09, 2021. https://scholarworks.rit.edu/theses/10354.

MLA Handbook (7th Edition):

Sankaran, Prashant. “Design and Simulation Analysis of Deep Learning Based Approaches and Multi-Attribute Algorithms for Warehouse Task Selection.” 2020. Web. 09 Mar 2021.

Vancouver:

Sankaran P. Design and Simulation Analysis of Deep Learning Based Approaches and Multi-Attribute Algorithms for Warehouse Task Selection. [Internet] [Masters thesis]. Rochester Institute of Technology; 2020. [cited 2021 Mar 09]. Available from: https://scholarworks.rit.edu/theses/10354.

Council of Science Editors:

Sankaran P. Design and Simulation Analysis of Deep Learning Based Approaches and Multi-Attribute Algorithms for Warehouse Task Selection. [Masters Thesis]. Rochester Institute of Technology; 2020. Available from: https://scholarworks.rit.edu/theses/10354


University of Illinois – Urbana-Champaign

12. Dave, Mihika. Multimodal machine translation.

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

 Over the past few years, there has been a lot of progress being made in machine translation through deep learning networks. But there is relatively… (more)

Subjects/Keywords: multimodal machine translation; neural machine translation; multi-task learning; image captioning

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APA (6th Edition):

Dave, M. (2018). Multimodal machine translation. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/101374

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):

Dave, Mihika. “Multimodal machine translation.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed March 09, 2021. http://hdl.handle.net/2142/101374.

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

MLA Handbook (7th Edition):

Dave, Mihika. “Multimodal machine translation.” 2018. Web. 09 Mar 2021.

Vancouver:

Dave M. Multimodal machine translation. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/2142/101374.

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

Council of Science Editors:

Dave M. Multimodal machine translation. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/101374

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


George Mason University

13. Naik, Azad. Using Multi-Task Learning For Large-Scale Document Classification .

Degree: 2013, George Mason University

Multi-Task Learning (MTL) involves learning of multiple tasks, jointly. It seeks to improve the generalization performance of each task by leveraging the relationships among the… (more)

Subjects/Keywords: Multi-Task Learning; classification; model selection; logistic regression; random projection (hashing)

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APA (6th Edition):

Naik, A. (2013). Using Multi-Task Learning For Large-Scale Document Classification . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/8479

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):

Naik, Azad. “Using Multi-Task Learning For Large-Scale Document Classification .” 2013. Thesis, George Mason University. Accessed March 09, 2021. http://hdl.handle.net/1920/8479.

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

MLA Handbook (7th Edition):

Naik, Azad. “Using Multi-Task Learning For Large-Scale Document Classification .” 2013. Web. 09 Mar 2021.

Vancouver:

Naik A. Using Multi-Task Learning For Large-Scale Document Classification . [Internet] [Thesis]. George Mason University; 2013. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/1920/8479.

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

Council of Science Editors:

Naik A. Using Multi-Task Learning For Large-Scale Document Classification . [Thesis]. George Mason University; 2013. Available from: http://hdl.handle.net/1920/8479

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


Delft University of Technology

14. Rentroia Pacheco, Barbara (author). Multi-task learning of transcriptomic signatures underlying cancer gene dependencies.

Degree: 2019, Delft University of Technology

Due to their altered genetic context, cancer cells can become dependent on specific genes for their survival. Such cancer-specific dependencies may represent promising therapeutic targets.… (more)

Subjects/Keywords: bioinformatics; multi-task learning; cancer dependencies; cancer genomics

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APA (6th Edition):

Rentroia Pacheco, B. (. (2019). Multi-task learning of transcriptomic signatures underlying cancer gene dependencies. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:2e793ece-4572-4bb6-83e3-541be467cb4f

Chicago Manual of Style (16th Edition):

Rentroia Pacheco, Barbara (author). “Multi-task learning of transcriptomic signatures underlying cancer gene dependencies.” 2019. Masters Thesis, Delft University of Technology. Accessed March 09, 2021. http://resolver.tudelft.nl/uuid:2e793ece-4572-4bb6-83e3-541be467cb4f.

MLA Handbook (7th Edition):

Rentroia Pacheco, Barbara (author). “Multi-task learning of transcriptomic signatures underlying cancer gene dependencies.” 2019. Web. 09 Mar 2021.

Vancouver:

Rentroia Pacheco B(. Multi-task learning of transcriptomic signatures underlying cancer gene dependencies. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 09]. Available from: http://resolver.tudelft.nl/uuid:2e793ece-4572-4bb6-83e3-541be467cb4f.

Council of Science Editors:

Rentroia Pacheco B(. Multi-task learning of transcriptomic signatures underlying cancer gene dependencies. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:2e793ece-4572-4bb6-83e3-541be467cb4f


University of Minnesota

15. Cai, Feng. Advanced learning approaches based on SVM+ methodology.

Degree: PhD, Electrical Engineering, 2011, University of Minnesota

 Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised learning applications, training data contains additional… (more)

Subjects/Keywords: feature selection; multi-task learning; SMO; SVM+; SVM+MTL; Electrical Engineering

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APA (6th Edition):

Cai, F. (2011). Advanced learning approaches based on SVM+ methodology. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/112973

Chicago Manual of Style (16th Edition):

Cai, Feng. “Advanced learning approaches based on SVM+ methodology.” 2011. Doctoral Dissertation, University of Minnesota. Accessed March 09, 2021. http://purl.umn.edu/112973.

MLA Handbook (7th Edition):

Cai, Feng. “Advanced learning approaches based on SVM+ methodology.” 2011. Web. 09 Mar 2021.

Vancouver:

Cai F. Advanced learning approaches based on SVM+ methodology. [Internet] [Doctoral dissertation]. University of Minnesota; 2011. [cited 2021 Mar 09]. Available from: http://purl.umn.edu/112973.

Council of Science Editors:

Cai F. Advanced learning approaches based on SVM+ methodology. [Doctoral Dissertation]. University of Minnesota; 2011. Available from: http://purl.umn.edu/112973


Virginia Tech

16. Li, Yifu. Data Filtering and Modeling for Smart Manufacturing Network.

Degree: PhD, Industrial and Systems Engineering, 2020, Virginia Tech

 The advancement of the Internet-of-Things (IoT) integrates manufacturing processes and equipment into a network. Practitioners analyze and apply the data generated from the network to… (more)

Subjects/Keywords: Data Filtering; Distributed Filtering and Modeling; Multi-task Learning

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APA (6th Edition):

Li, Y. (2020). Data Filtering and Modeling for Smart Manufacturing Network. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/99713

Chicago Manual of Style (16th Edition):

Li, Yifu. “Data Filtering and Modeling for Smart Manufacturing Network.” 2020. Doctoral Dissertation, Virginia Tech. Accessed March 09, 2021. http://hdl.handle.net/10919/99713.

MLA Handbook (7th Edition):

Li, Yifu. “Data Filtering and Modeling for Smart Manufacturing Network.” 2020. Web. 09 Mar 2021.

Vancouver:

Li Y. Data Filtering and Modeling for Smart Manufacturing Network. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10919/99713.

Council of Science Editors:

Li Y. Data Filtering and Modeling for Smart Manufacturing Network. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/99713


Virginia Tech

17. Nallendran, Vignesh Raja. Predicting Performance Run-time Metrics in Fog Manufacturing using Multi-task Learning.

Degree: MS, Industrial and Systems Engineering, 2021, Virginia Tech

 Smart manufacturing aims at utilizing Internet of things (IoT), data analytics, cloud computing, etc. to handle varying market demand without compromising the productivity or quality… (more)

Subjects/Keywords: Fog computing; Fog manufacturing; Multi-task learning; Run-time metrics

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APA (6th Edition):

Nallendran, V. R. (2021). Predicting Performance Run-time Metrics in Fog Manufacturing using Multi-task Learning. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/102501

Chicago Manual of Style (16th Edition):

Nallendran, Vignesh Raja. “Predicting Performance Run-time Metrics in Fog Manufacturing using Multi-task Learning.” 2021. Masters Thesis, Virginia Tech. Accessed March 09, 2021. http://hdl.handle.net/10919/102501.

MLA Handbook (7th Edition):

Nallendran, Vignesh Raja. “Predicting Performance Run-time Metrics in Fog Manufacturing using Multi-task Learning.” 2021. Web. 09 Mar 2021.

Vancouver:

Nallendran VR. Predicting Performance Run-time Metrics in Fog Manufacturing using Multi-task Learning. [Internet] [Masters thesis]. Virginia Tech; 2021. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10919/102501.

Council of Science Editors:

Nallendran VR. Predicting Performance Run-time Metrics in Fog Manufacturing using Multi-task Learning. [Masters Thesis]. Virginia Tech; 2021. Available from: http://hdl.handle.net/10919/102501


University of Sydney

18. Li, Jizhizi. End-to-end Animal Matting .

Degree: 2020, University of Sydney

 Image matting is a widely studied low-level vision problem that aims to provide a detailed foreground alpha matte from a single image, benefiting a wide… (more)

Subjects/Keywords: image matting; animal; segmentation; multi-task; deep learning

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APA (6th Edition):

Li, J. (2020). End-to-end Animal Matting . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/22897

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, Jizhizi. “End-to-end Animal Matting .” 2020. Thesis, University of Sydney. Accessed March 09, 2021. http://hdl.handle.net/2123/22897.

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

MLA Handbook (7th Edition):

Li, Jizhizi. “End-to-end Animal Matting .” 2020. Web. 09 Mar 2021.

Vancouver:

Li J. End-to-end Animal Matting . [Internet] [Thesis]. University of Sydney; 2020. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/2123/22897.

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

Council of Science Editors:

Li J. End-to-end Animal Matting . [Thesis]. University of Sydney; 2020. Available from: http://hdl.handle.net/2123/22897

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


UCLA

19. Chen, Wan-Ping. Bayesian Method for Support Union Recovery in Multivariate Multi-Response Linear Regression.

Degree: Statistics, 2015, UCLA

 Sparse modeling has become a particularly important and quickly developing topic in many applications of statistics, machine learning, and signal processing. The main objective of… (more)

Subjects/Keywords: Statistics; Bayesian; Multi-response Linear Regression; Multi-task Learning; Support Union Recovery

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APA (6th Edition):

Chen, W. (2015). Bayesian Method for Support Union Recovery in Multivariate Multi-Response Linear Regression. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/7174k4ps

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, Wan-Ping. “Bayesian Method for Support Union Recovery in Multivariate Multi-Response Linear Regression.” 2015. Thesis, UCLA. Accessed March 09, 2021. http://www.escholarship.org/uc/item/7174k4ps.

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

MLA Handbook (7th Edition):

Chen, Wan-Ping. “Bayesian Method for Support Union Recovery in Multivariate Multi-Response Linear Regression.” 2015. Web. 09 Mar 2021.

Vancouver:

Chen W. Bayesian Method for Support Union Recovery in Multivariate Multi-Response Linear Regression. [Internet] [Thesis]. UCLA; 2015. [cited 2021 Mar 09]. Available from: http://www.escholarship.org/uc/item/7174k4ps.

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

Council of Science Editors:

Chen W. Bayesian Method for Support Union Recovery in Multivariate Multi-Response Linear Regression. [Thesis]. UCLA; 2015. Available from: http://www.escholarship.org/uc/item/7174k4ps

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


University of Melbourne

20. Karunaratne, Pasan Manura. Scalable and accurate forecasting for smart cities.

Degree: 2018, University of Melbourne

 Cities are getting bigger, better and smarter. The increased connectivity of people and devices and the availability of cheap sensors has led to a surge… (more)

Subjects/Keywords: smart cities; time series; multi-step forecasting; multi-task learning; gaussian process regression

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APA (6th Edition):

Karunaratne, P. M. (2018). Scalable and accurate forecasting for smart cities. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/214669

Chicago Manual of Style (16th Edition):

Karunaratne, Pasan Manura. “Scalable and accurate forecasting for smart cities.” 2018. Doctoral Dissertation, University of Melbourne. Accessed March 09, 2021. http://hdl.handle.net/11343/214669.

MLA Handbook (7th Edition):

Karunaratne, Pasan Manura. “Scalable and accurate forecasting for smart cities.” 2018. Web. 09 Mar 2021.

Vancouver:

Karunaratne PM. Scalable and accurate forecasting for smart cities. [Internet] [Doctoral dissertation]. University of Melbourne; 2018. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/11343/214669.

Council of Science Editors:

Karunaratne PM. Scalable and accurate forecasting for smart cities. [Doctoral Dissertation]. University of Melbourne; 2018. Available from: http://hdl.handle.net/11343/214669


Carnegie Mellon University

21. Zhang, Yi. Learning with Limited Supervision by Input and Output Coding.

Degree: 2012, Carnegie Mellon University

 In many real-world applications of supervised learning, only a limited number of labeled examples are available because the cost of obtaining high-quality examples is high.… (more)

Subjects/Keywords: regularization; error-correcting output codes; supervised learning; semi-supervised learning; multi-task learning; multi-label classification; dimensionality reduction; Computer Sciences

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APA (6th Edition):

Zhang, Y. (2012). Learning with Limited Supervision by Input and Output Coding. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/156

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

Chicago Manual of Style (16th Edition):

Zhang, Yi. “Learning with Limited Supervision by Input and Output Coding.” 2012. Thesis, Carnegie Mellon University. Accessed March 09, 2021. http://repository.cmu.edu/dissertations/156.

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

MLA Handbook (7th Edition):

Zhang, Yi. “Learning with Limited Supervision by Input and Output Coding.” 2012. Web. 09 Mar 2021.

Vancouver:

Zhang Y. Learning with Limited Supervision by Input and Output Coding. [Internet] [Thesis]. Carnegie Mellon University; 2012. [cited 2021 Mar 09]. Available from: http://repository.cmu.edu/dissertations/156.

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

Council of Science Editors:

Zhang Y. Learning with Limited Supervision by Input and Output Coding. [Thesis]. Carnegie Mellon University; 2012. Available from: http://repository.cmu.edu/dissertations/156

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


University of Kansas

22. Zhang, Jintao. Multi-task and Multi-view Learning for Predicting Adverse Drug Reactions.

Degree: PhD, Information Technology, 2012, University of Kansas

 Adverse drug reactions (ADRs) present a major concern for drug safety and are a major obstacle in modern drug development. They account for about one-third… (more)

Subjects/Keywords: Bioinformatics; Information technology; adverse drug reaction; boosting; co-regularization; inductive learning; multi-task learning; multi-view learning

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APA (6th Edition):

Zhang, J. (2012). Multi-task and Multi-view Learning for Predicting Adverse Drug Reactions. (Doctoral Dissertation). University of Kansas. Retrieved from http://hdl.handle.net/1808/18634

Chicago Manual of Style (16th Edition):

Zhang, Jintao. “Multi-task and Multi-view Learning for Predicting Adverse Drug Reactions.” 2012. Doctoral Dissertation, University of Kansas. Accessed March 09, 2021. http://hdl.handle.net/1808/18634.

MLA Handbook (7th Edition):

Zhang, Jintao. “Multi-task and Multi-view Learning for Predicting Adverse Drug Reactions.” 2012. Web. 09 Mar 2021.

Vancouver:

Zhang J. Multi-task and Multi-view Learning for Predicting Adverse Drug Reactions. [Internet] [Doctoral dissertation]. University of Kansas; 2012. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/1808/18634.

Council of Science Editors:

Zhang J. Multi-task and Multi-view Learning for Predicting Adverse Drug Reactions. [Doctoral Dissertation]. University of Kansas; 2012. Available from: http://hdl.handle.net/1808/18634


Georgia Tech

23. Lu, Jiasen. Visually grounded language understanding and generation.

Degree: PhD, Computer Science, 2020, Georgia Tech

 The world around us involves multiple modalities  – we see objects, feel texture, hear sounds, smell odors and so on. In order for Artificial Intelligence… (more)

Subjects/Keywords: Computer vision; Natural language processing; Visual question answering; Multi-task learning; Deep learning

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APA (6th Edition):

Lu, J. (2020). Visually grounded language understanding and generation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62745

Chicago Manual of Style (16th Edition):

Lu, Jiasen. “Visually grounded language understanding and generation.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 09, 2021. http://hdl.handle.net/1853/62745.

MLA Handbook (7th Edition):

Lu, Jiasen. “Visually grounded language understanding and generation.” 2020. Web. 09 Mar 2021.

Vancouver:

Lu J. Visually grounded language understanding and generation. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/1853/62745.

Council of Science Editors:

Lu J. Visually grounded language understanding and generation. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62745


University of Arizona

24. Meyer, Josh. Multi-Task and Transfer Learning in Low-Resource Speech Recognition .

Degree: 2019, University of Arizona

 This thesis investigates methods for Acoustic Modeling in Automatic Speech Recog- nition, assuming limited access to training data in the target domain. The Acoustic Models… (more)

Subjects/Keywords: Automatic Speech Recognition; Deep Neural Networks; Low-Resource Languages; Multi-Task Learning; Transfer Learning

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APA (6th Edition):

Meyer, J. (2019). Multi-Task and Transfer Learning in Low-Resource Speech Recognition . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/634249

Chicago Manual of Style (16th Edition):

Meyer, Josh. “Multi-Task and Transfer Learning in Low-Resource Speech Recognition .” 2019. Doctoral Dissertation, University of Arizona. Accessed March 09, 2021. http://hdl.handle.net/10150/634249.

MLA Handbook (7th Edition):

Meyer, Josh. “Multi-Task and Transfer Learning in Low-Resource Speech Recognition .” 2019. Web. 09 Mar 2021.

Vancouver:

Meyer J. Multi-Task and Transfer Learning in Low-Resource Speech Recognition . [Internet] [Doctoral dissertation]. University of Arizona; 2019. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10150/634249.

Council of Science Editors:

Meyer J. Multi-Task and Transfer Learning in Low-Resource Speech Recognition . [Doctoral Dissertation]. University of Arizona; 2019. Available from: http://hdl.handle.net/10150/634249


University of Pennsylvania

25. Isele, David. Lifelong Reinforcement Learning On Mobile Robots.

Degree: 2018, University of Pennsylvania

 Machine learning has shown tremendous growth in the past decades, unlocking new capabilities in a variety of fields including computer vision, natural language processing, and… (more)

Subjects/Keywords: lifelong machine learning; multi-task learning; transfer; Artificial Intelligence and Robotics; Robotics

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

APA (6th Edition):

Isele, D. (2018). Lifelong Reinforcement Learning On Mobile Robots. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/3290

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):

Isele, David. “Lifelong Reinforcement Learning On Mobile Robots.” 2018. Thesis, University of Pennsylvania. Accessed March 09, 2021. https://repository.upenn.edu/edissertations/3290.

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

MLA Handbook (7th Edition):

Isele, David. “Lifelong Reinforcement Learning On Mobile Robots.” 2018. Web. 09 Mar 2021.

Vancouver:

Isele D. Lifelong Reinforcement Learning On Mobile Robots. [Internet] [Thesis]. University of Pennsylvania; 2018. [cited 2021 Mar 09]. Available from: https://repository.upenn.edu/edissertations/3290.

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

Council of Science Editors:

Isele D. Lifelong Reinforcement Learning On Mobile Robots. [Thesis]. University of Pennsylvania; 2018. Available from: https://repository.upenn.edu/edissertations/3290

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


University of Adelaide

26. Nekrasov, Vladimir. Semantic Image Segmentation and Other Dense Per-Pixel Tasks: Practical Approaches.

Degree: 2020, University of Adelaide

 Computer vision-based and deep learning-driven applications and devices are now a part of our everyday life: from modern smartphones with an ever increasing number of… (more)

Subjects/Keywords: Semantic segmentation; deep learning; real-time inference; neural architecture search; multi-task learning

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APA (6th Edition):

Nekrasov, V. (2020). Semantic Image Segmentation and Other Dense Per-Pixel Tasks: Practical Approaches. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/129333

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):

Nekrasov, Vladimir. “Semantic Image Segmentation and Other Dense Per-Pixel Tasks: Practical Approaches.” 2020. Thesis, University of Adelaide. Accessed March 09, 2021. http://hdl.handle.net/2440/129333.

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

MLA Handbook (7th Edition):

Nekrasov, Vladimir. “Semantic Image Segmentation and Other Dense Per-Pixel Tasks: Practical Approaches.” 2020. Web. 09 Mar 2021.

Vancouver:

Nekrasov V. Semantic Image Segmentation and Other Dense Per-Pixel Tasks: Practical Approaches. [Internet] [Thesis]. University of Adelaide; 2020. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/2440/129333.

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

Council of Science Editors:

Nekrasov V. Semantic Image Segmentation and Other Dense Per-Pixel Tasks: Practical Approaches. [Thesis]. University of Adelaide; 2020. Available from: http://hdl.handle.net/2440/129333

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


Georgia Tech

27. Mehta, Nishant A. On sparse representations and new meta-learning paradigms for representation learning.

Degree: PhD, Computer Science, 2013, Georgia Tech

 Given the "right" representation, learning is easy. This thesis studies representation learning and meta-learning, with a special focus on sparse representations. Meta-learning is fundamental to… (more)

Subjects/Keywords: Learning theory; Data-dependent complexity; Luckiness; Dictionary learning; Sparse coding; Lasso; Multi-task learning; Meta-learning; Learning to learn

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

APA (6th Edition):

Mehta, N. A. (2013). On sparse representations and new meta-learning paradigms for representation learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52159

Chicago Manual of Style (16th Edition):

Mehta, Nishant A. “On sparse representations and new meta-learning paradigms for representation learning.” 2013. Doctoral Dissertation, Georgia Tech. Accessed March 09, 2021. http://hdl.handle.net/1853/52159.

MLA Handbook (7th Edition):

Mehta, Nishant A. “On sparse representations and new meta-learning paradigms for representation learning.” 2013. Web. 09 Mar 2021.

Vancouver:

Mehta NA. On sparse representations and new meta-learning paradigms for representation learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/1853/52159.

Council of Science Editors:

Mehta NA. On sparse representations and new meta-learning paradigms for representation learning. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/52159


Linköping University

28. Rehman, Obaid Ur. Multi-Task Convolutional Learning for Flame Characterization.

Degree: The Division of Statistics and Machine Learning, 2020, Linköping University

This thesis explores multi-task learning for combustion flame characterization i.e to learn different characteristics of the combustion flame. We propose a multi-task convolutional neural… (more)

Subjects/Keywords: Multi task learning; multi task convolutional learning; transfer learning; VGG16; CNN; convolutional neural networks; MTL; MTL CNN; Computer Systems; Datorsystem; Probability Theory and Statistics; Sannolikhetsteori och statistik

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APA (6th Edition):

Rehman, O. U. (2020). Multi-Task Convolutional Learning for Flame Characterization. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166054

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):

Rehman, Obaid Ur. “Multi-Task Convolutional Learning for Flame Characterization.” 2020. Thesis, Linköping University. Accessed March 09, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166054.

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

MLA Handbook (7th Edition):

Rehman, Obaid Ur. “Multi-Task Convolutional Learning for Flame Characterization.” 2020. Web. 09 Mar 2021.

Vancouver:

Rehman OU. Multi-Task Convolutional Learning for Flame Characterization. [Internet] [Thesis]. Linköping University; 2020. [cited 2021 Mar 09]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166054.

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

Council of Science Editors:

Rehman OU. Multi-Task Convolutional Learning for Flame Characterization. [Thesis]. Linköping University; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166054

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


Wayne State University

29. Al-Stouhi, Samir. Learning With An Insufficient Supply Of Data Via Knowledge Transfer And Sharing.

Degree: PhD, Electrical and Computer Engineering, 2013, Wayne State University

  As machine learning methods extend to more complex and diverse set of problems, situations arise where the complexity and availability of data presents a… (more)

Subjects/Keywords: Data Mining; Imbalanced Learning; Machine Learning; Multi-Task Learning; Rare Dataset; Transfer Learning; Computer Engineering; Computer Sciences

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

APA (6th Edition):

Al-Stouhi, S. (2013). Learning With An Insufficient Supply Of Data Via Knowledge Transfer And Sharing. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/829

Chicago Manual of Style (16th Edition):

Al-Stouhi, Samir. “Learning With An Insufficient Supply Of Data Via Knowledge Transfer And Sharing.” 2013. Doctoral Dissertation, Wayne State University. Accessed March 09, 2021. https://digitalcommons.wayne.edu/oa_dissertations/829.

MLA Handbook (7th Edition):

Al-Stouhi, Samir. “Learning With An Insufficient Supply Of Data Via Knowledge Transfer And Sharing.” 2013. Web. 09 Mar 2021.

Vancouver:

Al-Stouhi S. Learning With An Insufficient Supply Of Data Via Knowledge Transfer And Sharing. [Internet] [Doctoral dissertation]. Wayne State University; 2013. [cited 2021 Mar 09]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/829.

Council of Science Editors:

Al-Stouhi S. Learning With An Insufficient Supply Of Data Via Knowledge Transfer And Sharing. [Doctoral Dissertation]. Wayne State University; 2013. Available from: https://digitalcommons.wayne.edu/oa_dissertations/829


Rochester Institute of Technology

30. Dangi, Shusil. Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images.

Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2019, Rochester Institute of Technology

  Segmentation of the heart structures helps compute the cardiac contractile function quantified via the systolic and diastolic volumes, ejection fraction, and myocardial mass, representing… (more)

Subjects/Keywords: Cardiac cine MRI; Cardiac ultrasound; Convolutional neural network; Image registration; Image segmentation; Multi-task learning

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

APA (6th Edition):

Dangi, S. (2019). Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10271

Chicago Manual of Style (16th Edition):

Dangi, Shusil. “Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images.” 2019. Doctoral Dissertation, Rochester Institute of Technology. Accessed March 09, 2021. https://scholarworks.rit.edu/theses/10271.

MLA Handbook (7th Edition):

Dangi, Shusil. “Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images.” 2019. Web. 09 Mar 2021.

Vancouver:

Dangi S. Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2019. [cited 2021 Mar 09]. Available from: https://scholarworks.rit.edu/theses/10271.

Council of Science Editors:

Dangi S. Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images. [Doctoral Dissertation]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10271

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