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You searched for subject:(Budgeted learning). Showing records 1 – 4 of 4 total matches.

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Washington University in St. Louis

1. Kusner, Matt J. Learning in the Real World: Constraints on Cost, Space, and Privacy.

Degree: PhD, Computer Science & Engineering, 2016, Washington University in St. Louis

  The sheer demand for machine learning in fields as varied as: healthcare, web-search ranking, factory automation, collision prediction, spam filtering, and many others, frequently… (more)

Subjects/Keywords: budgeted learning; differential privacy; machine learning; model compression; resource efficient learning; Computer Engineering; Engineering

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APA (6th Edition):

Kusner, M. J. (2016). Learning in the Real World: Constraints on Cost, Space, and Privacy. (Doctoral Dissertation). Washington University in St. Louis. Retrieved from https://openscholarship.wustl.edu/eng_etds/305

Chicago Manual of Style (16th Edition):

Kusner, Matt J. “Learning in the Real World: Constraints on Cost, Space, and Privacy.” 2016. Doctoral Dissertation, Washington University in St. Louis. Accessed April 24, 2019. https://openscholarship.wustl.edu/eng_etds/305.

MLA Handbook (7th Edition):

Kusner, Matt J. “Learning in the Real World: Constraints on Cost, Space, and Privacy.” 2016. Web. 24 Apr 2019.

Vancouver:

Kusner MJ. Learning in the Real World: Constraints on Cost, Space, and Privacy. [Internet] [Doctoral dissertation]. Washington University in St. Louis; 2016. [cited 2019 Apr 24]. Available from: https://openscholarship.wustl.edu/eng_etds/305.

Council of Science Editors:

Kusner MJ. Learning in the Real World: Constraints on Cost, Space, and Privacy. [Doctoral Dissertation]. Washington University in St. Louis; 2016. Available from: https://openscholarship.wustl.edu/eng_etds/305

2. Aklil, Nassim. Apprentissage actif sous contrainte de budget en robotique et en neurosciences computationnelles. Localisation robotique et modélisation comportementale en environnement non stationnaire : Active learning under budget constraint in robotics and computational neuroscience. Robotic localization and behavioral modeling in non-stationary environment.

Degree: Docteur es, Intelligence Artificielle et Robotique, 2017, Université Pierre et Marie Curie – Paris VI

La prise de décision est un domaine très étudié en sciences, que ce soit en neurosciences pour comprendre les processus sous tendant la prise de… (more)

Subjects/Keywords: Apprentissage par renforcement; Apprentissage budgétisé; Apprentissage profond; Neurosciences computationnelles; Compromis exploration/exploitation; Policy gradient; Budgeted learning; Computational neuroscience; Deep learning; 629.89

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

APA (6th Edition):

Aklil, N. (2017). Apprentissage actif sous contrainte de budget en robotique et en neurosciences computationnelles. Localisation robotique et modélisation comportementale en environnement non stationnaire : Active learning under budget constraint in robotics and computational neuroscience. Robotic localization and behavioral modeling in non-stationary environment. (Doctoral Dissertation). Université Pierre et Marie Curie – Paris VI. Retrieved from http://www.theses.fr/2017PA066225

Chicago Manual of Style (16th Edition):

Aklil, Nassim. “Apprentissage actif sous contrainte de budget en robotique et en neurosciences computationnelles. Localisation robotique et modélisation comportementale en environnement non stationnaire : Active learning under budget constraint in robotics and computational neuroscience. Robotic localization and behavioral modeling in non-stationary environment.” 2017. Doctoral Dissertation, Université Pierre et Marie Curie – Paris VI. Accessed April 24, 2019. http://www.theses.fr/2017PA066225.

MLA Handbook (7th Edition):

Aklil, Nassim. “Apprentissage actif sous contrainte de budget en robotique et en neurosciences computationnelles. Localisation robotique et modélisation comportementale en environnement non stationnaire : Active learning under budget constraint in robotics and computational neuroscience. Robotic localization and behavioral modeling in non-stationary environment.” 2017. Web. 24 Apr 2019.

Vancouver:

Aklil N. Apprentissage actif sous contrainte de budget en robotique et en neurosciences computationnelles. Localisation robotique et modélisation comportementale en environnement non stationnaire : Active learning under budget constraint in robotics and computational neuroscience. Robotic localization and behavioral modeling in non-stationary environment. [Internet] [Doctoral dissertation]. Université Pierre et Marie Curie – Paris VI; 2017. [cited 2019 Apr 24]. Available from: http://www.theses.fr/2017PA066225.

Council of Science Editors:

Aklil N. Apprentissage actif sous contrainte de budget en robotique et en neurosciences computationnelles. Localisation robotique et modélisation comportementale en environnement non stationnaire : Active learning under budget constraint in robotics and computational neuroscience. Robotic localization and behavioral modeling in non-stationary environment. [Doctoral Dissertation]. Université Pierre et Marie Curie – Paris VI; 2017. Available from: http://www.theses.fr/2017PA066225


University of Texas – Austin

3. Vijayanarasimhan, Sudheendra. Active visual category learning.

Degree: Computer Sciences, 2011, University of Texas – Austin

 Visual recognition research develops algorithms and representations to autonomously recognize visual entities such as objects, actions, and attributes. The traditional protocol involves manually collecting training… (more)

Subjects/Keywords: Artificial intelligence; Active learning; Object recognition; Object detection; Cost-sensitive learning; Multi-level learning; Budgeted learning; Large-scale active learning; Live learning; Machine learning; Visual recognition system

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

APA (6th Edition):

Vijayanarasimhan, S. (2011). Active visual category learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-05-3014

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):

Vijayanarasimhan, Sudheendra. “Active visual category learning.” 2011. Thesis, University of Texas – Austin. Accessed April 24, 2019. http://hdl.handle.net/2152/ETD-UT-2011-05-3014.

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

MLA Handbook (7th Edition):

Vijayanarasimhan, Sudheendra. “Active visual category learning.” 2011. Web. 24 Apr 2019.

Vancouver:

Vijayanarasimhan S. Active visual category learning. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3014.

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

Council of Science Editors:

Vijayanarasimhan S. Active visual category learning. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3014

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


Université Paris-Sud – Paris XI

4. Benbouzid, Djalel. Sequential prediction for budgeted learning : Application to trigger design : Prédiction séquentielle pour l'apprentissage budgété : Application à la conception de trigger.

Degree: Docteur es, Informatique, 2014, Université Paris-Sud – Paris XI

 Cette thèse aborde le problème de classification en apprentissage statistique sous un angle nouveau en rajoutant une dimension séquentielle au processus de classification. En particulier,… (more)

Subjects/Keywords: Apprentissage statistique; Classification; Classification rapide; Apprentissage à contraintes de budget; Classification séquentielle; Trigger; LHCb; Cascades; Apprentissage par renforcement; Machine learning; Classification; Fast classification; Budgeted classification; Sequential classification; Trigger; LHCb; Cascades; Reinforcement learning

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

APA (6th Edition):

Benbouzid, D. (2014). Sequential prediction for budgeted learning : Application to trigger design : Prédiction séquentielle pour l'apprentissage budgété : Application à la conception de trigger. (Doctoral Dissertation). Université Paris-Sud – Paris XI. Retrieved from http://www.theses.fr/2014PA112031

Chicago Manual of Style (16th Edition):

Benbouzid, Djalel. “Sequential prediction for budgeted learning : Application to trigger design : Prédiction séquentielle pour l'apprentissage budgété : Application à la conception de trigger.” 2014. Doctoral Dissertation, Université Paris-Sud – Paris XI. Accessed April 24, 2019. http://www.theses.fr/2014PA112031.

MLA Handbook (7th Edition):

Benbouzid, Djalel. “Sequential prediction for budgeted learning : Application to trigger design : Prédiction séquentielle pour l'apprentissage budgété : Application à la conception de trigger.” 2014. Web. 24 Apr 2019.

Vancouver:

Benbouzid D. Sequential prediction for budgeted learning : Application to trigger design : Prédiction séquentielle pour l'apprentissage budgété : Application à la conception de trigger. [Internet] [Doctoral dissertation]. Université Paris-Sud – Paris XI; 2014. [cited 2019 Apr 24]. Available from: http://www.theses.fr/2014PA112031.

Council of Science Editors:

Benbouzid D. Sequential prediction for budgeted learning : Application to trigger design : Prédiction séquentielle pour l'apprentissage budgété : Application à la conception de trigger. [Doctoral Dissertation]. Université Paris-Sud – Paris XI; 2014. Available from: http://www.theses.fr/2014PA112031

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