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You searched for subject:(Network architecture search). Showing records 1 – 6 of 6 total matches.

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Utah State University

1. Oswal, Vipul Kantilal. Pathway Pioneer: Heterogenous Server Architecture for Scientific Visualization and Pathway Search in Metabolic Network Using Informed Search.

Degree: MS, Computer Science, 2014, Utah State University

  There is a huge demand for analysis and visualization of the biological models. PathwayPioneer is a web-based tool to analyze and visually represent complex… (more)

Subjects/Keywords: Heterogeneous Server Architecture; Scientific Visualization; Pathway Search; Metabolic Network; Computer Sciences

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APA (6th Edition):

Oswal, V. K. (2014). Pathway Pioneer: Heterogenous Server Architecture for Scientific Visualization and Pathway Search in Metabolic Network Using Informed Search. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/2775

Chicago Manual of Style (16th Edition):

Oswal, Vipul Kantilal. “Pathway Pioneer: Heterogenous Server Architecture for Scientific Visualization and Pathway Search in Metabolic Network Using Informed Search.” 2014. Masters Thesis, Utah State University. Accessed August 15, 2020. https://digitalcommons.usu.edu/etd/2775.

MLA Handbook (7th Edition):

Oswal, Vipul Kantilal. “Pathway Pioneer: Heterogenous Server Architecture for Scientific Visualization and Pathway Search in Metabolic Network Using Informed Search.” 2014. Web. 15 Aug 2020.

Vancouver:

Oswal VK. Pathway Pioneer: Heterogenous Server Architecture for Scientific Visualization and Pathway Search in Metabolic Network Using Informed Search. [Internet] [Masters thesis]. Utah State University; 2014. [cited 2020 Aug 15]. Available from: https://digitalcommons.usu.edu/etd/2775.

Council of Science Editors:

Oswal VK. Pathway Pioneer: Heterogenous Server Architecture for Scientific Visualization and Pathway Search in Metabolic Network Using Informed Search. [Masters Thesis]. Utah State University; 2014. Available from: https://digitalcommons.usu.edu/etd/2775

2. Rawal, Aditya, Ph. D. in computer science. Discovering gated recurrent neural network architectures.

Degree: PhD, Computer science, 2019, University of Texas – Austin

 Reinforcement Learning agent networks with memory are a key component in solving POMDP tasks. Gated recurrent networks such as those composed of Long Short-Term Memory… (more)

Subjects/Keywords: Recurrent neural networks; Neuroevolution; Network architecture search; Meta-learning; Reinforcement learning; Language modeling; Music modeling

…discusses previous work in gated recurrent network architecture search. Chapter 3 looks at RL… …that the search space of architecture is large. ( Chung et al. (2015))… …search for both network structure and weights in a non-parametric manner. NEAT gradually… …optimizing the network architecture using a meta-learning algorithm like Bayesian optimization… …layer deep network (see Figure 2.2). Evolution enables search for deeper and larger… 

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APA (6th Edition):

Rawal, Aditya, P. D. i. c. s. (2019). Discovering gated recurrent neural network architectures. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/72839

Chicago Manual of Style (16th Edition):

Rawal, Aditya, Ph D in computer science. “Discovering gated recurrent neural network architectures.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed August 15, 2020. http://hdl.handle.net/2152/72839.

MLA Handbook (7th Edition):

Rawal, Aditya, Ph D in computer science. “Discovering gated recurrent neural network architectures.” 2019. Web. 15 Aug 2020.

Vancouver:

Rawal, Aditya PDics. Discovering gated recurrent neural network architectures. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Aug 15]. Available from: http://hdl.handle.net/2152/72839.

Council of Science Editors:

Rawal, Aditya PDics. Discovering gated recurrent neural network architectures. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://hdl.handle.net/2152/72839


University of Manchester

3. Rodrigues, Crefeda. Efficient execution of convolutional neural networks on low powered heterogeneous systems.

Degree: PhD, 2020, University of Manchester

 Energy-efficient machine learning has been gaining interest due to the increase use of of machine learning, in particular deep learning, in applications that run on… (more)

Subjects/Keywords: Neural Architecture Search; Performance; Power estimation; Snapdragon 820; Jetson TX1; Neural Network Accelerators; Task graph; Energy efficiency; ConvNet; Dataflow mapping; Mobile Systems; Low power; Convolutional Neural Networks; CNN; Deep Learning; Energy; Energy prediction; Power measurement; Power

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APA (6th Edition):

Rodrigues, C. (2020). Efficient execution of convolutional neural networks on low powered heterogeneous systems. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/efficient-execution-of-convolutional-neural-networks-on-low-powered-heterogeneous-systems(a45af965-109b-4d4d-b72e-67a693787f58).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.804125

Chicago Manual of Style (16th Edition):

Rodrigues, Crefeda. “Efficient execution of convolutional neural networks on low powered heterogeneous systems.” 2020. Doctoral Dissertation, University of Manchester. Accessed August 15, 2020. https://www.research.manchester.ac.uk/portal/en/theses/efficient-execution-of-convolutional-neural-networks-on-low-powered-heterogeneous-systems(a45af965-109b-4d4d-b72e-67a693787f58).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.804125.

MLA Handbook (7th Edition):

Rodrigues, Crefeda. “Efficient execution of convolutional neural networks on low powered heterogeneous systems.” 2020. Web. 15 Aug 2020.

Vancouver:

Rodrigues C. Efficient execution of convolutional neural networks on low powered heterogeneous systems. [Internet] [Doctoral dissertation]. University of Manchester; 2020. [cited 2020 Aug 15]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/efficient-execution-of-convolutional-neural-networks-on-low-powered-heterogeneous-systems(a45af965-109b-4d4d-b72e-67a693787f58).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.804125.

Council of Science Editors:

Rodrigues C. Efficient execution of convolutional neural networks on low powered heterogeneous systems. [Doctoral Dissertation]. University of Manchester; 2020. Available from: https://www.research.manchester.ac.uk/portal/en/theses/efficient-execution-of-convolutional-neural-networks-on-low-powered-heterogeneous-systems(a45af965-109b-4d4d-b72e-67a693787f58).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.804125


University of New Mexico

4. Schulte, Eric. Neutral Networks of Real-World Programs and their Application to Automated Software Evolution.

Degree: Department of Computer Science, 2014, University of New Mexico

 The existing software development ecosystem is the product of evolutionary forces, and consequently real-world software is amenable to improvement through automated evolutionary techniques. This dissertation… (more)

Subjects/Keywords: software engineering; search based software engineering; software mutational robustness; mutational robustness; evolutionary computation; genetic algorithm; genetic programming; optimization; automated program repair; automated software engineering; bug repair; fitness landscape; instruction set architecture; neutral network

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

APA (6th Edition):

Schulte, E. (2014). Neutral Networks of Real-World Programs and their Application to Automated Software Evolution. (Doctoral Dissertation). University of New Mexico. Retrieved from http://hdl.handle.net/1928/25819

Chicago Manual of Style (16th Edition):

Schulte, Eric. “Neutral Networks of Real-World Programs and their Application to Automated Software Evolution.” 2014. Doctoral Dissertation, University of New Mexico. Accessed August 15, 2020. http://hdl.handle.net/1928/25819.

MLA Handbook (7th Edition):

Schulte, Eric. “Neutral Networks of Real-World Programs and their Application to Automated Software Evolution.” 2014. Web. 15 Aug 2020.

Vancouver:

Schulte E. Neutral Networks of Real-World Programs and their Application to Automated Software Evolution. [Internet] [Doctoral dissertation]. University of New Mexico; 2014. [cited 2020 Aug 15]. Available from: http://hdl.handle.net/1928/25819.

Council of Science Editors:

Schulte E. Neutral Networks of Real-World Programs and their Application to Automated Software Evolution. [Doctoral Dissertation]. University of New Mexico; 2014. Available from: http://hdl.handle.net/1928/25819


University of Manchester

5. Rodrigues, Crefeda Faviola. Efficient Execution of Convolutional Neural Networks on Low Powered Heterogeneous Systems.

Degree: 2020, University of Manchester

Energy-efficient machine learning has been gaining interest due to the increase use of of machine learning, in particular deep learning, in applications that run on… (more)

Subjects/Keywords: Deep Learning; Convolutional Neural Networks; CNN; Power; Energy; Energy prediction; Power measurement; Mobile Systems; Low power; ConvNet; Energy efficiency; Task graph; Neural Network Accelerators; Jetson TX1; Snapdragon 820; Power estimation; Performance; Dataflow mapping; Neural Architecture Search

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

APA (6th Edition):

Rodrigues, C. F. (2020). Efficient Execution of Convolutional Neural Networks on Low Powered Heterogeneous Systems. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323894

Chicago Manual of Style (16th Edition):

Rodrigues, Crefeda Faviola. “Efficient Execution of Convolutional Neural Networks on Low Powered Heterogeneous Systems.” 2020. Doctoral Dissertation, University of Manchester. Accessed August 15, 2020. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323894.

MLA Handbook (7th Edition):

Rodrigues, Crefeda Faviola. “Efficient Execution of Convolutional Neural Networks on Low Powered Heterogeneous Systems.” 2020. Web. 15 Aug 2020.

Vancouver:

Rodrigues CF. Efficient Execution of Convolutional Neural Networks on Low Powered Heterogeneous Systems. [Internet] [Doctoral dissertation]. University of Manchester; 2020. [cited 2020 Aug 15]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323894.

Council of Science Editors:

Rodrigues CF. Efficient Execution of Convolutional Neural Networks on Low Powered Heterogeneous Systems. [Doctoral Dissertation]. University of Manchester; 2020. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323894

6. Rahman, Mahmudur. Data Verifications for Online Social Networks.

Degree: PhD, Computer Science, 2015, Florida International University

  Social networks are popular platforms that simplify user interaction and encourage collaboration. They collect large amounts of media from their users, often reported from… (more)

Subjects/Keywords: online social network; Data verification; Security; Video Authentication; Wearable sensors; Privacy; Fraudulent reviews; Malware; Search Rank fraud apps; Smart City; Public safety; Geosocial networks; Information Security; OS and Networks; Systems Architecture

…identify not only search rank fraud but also malware in Google Play, the most popular… …Private LCP Requirements . . . . . . . . . . . . . . . . . . . . 6.2.3 Geosocial Network… …network data. . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Crime and Census data… …32 3.2 YCrawl system architecture. YCrawl relies on a pool of servers and proxies to… …architecture. The CoReG module identiļ¬es suspicious, time related co-review behaviors. The RF module… 

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APA (6th Edition):

Rahman, M. (2015). Data Verifications for Online Social Networks. (Doctoral Dissertation). Florida International University. Retrieved from https://digitalcommons.fiu.edu/etd/2299 ; 10.25148/etd.FIDC000196 ; FIDC000196

Chicago Manual of Style (16th Edition):

Rahman, Mahmudur. “Data Verifications for Online Social Networks.” 2015. Doctoral Dissertation, Florida International University. Accessed August 15, 2020. https://digitalcommons.fiu.edu/etd/2299 ; 10.25148/etd.FIDC000196 ; FIDC000196.

MLA Handbook (7th Edition):

Rahman, Mahmudur. “Data Verifications for Online Social Networks.” 2015. Web. 15 Aug 2020.

Vancouver:

Rahman M. Data Verifications for Online Social Networks. [Internet] [Doctoral dissertation]. Florida International University; 2015. [cited 2020 Aug 15]. Available from: https://digitalcommons.fiu.edu/etd/2299 ; 10.25148/etd.FIDC000196 ; FIDC000196.

Council of Science Editors:

Rahman M. Data Verifications for Online Social Networks. [Doctoral Dissertation]. Florida International University; 2015. Available from: https://digitalcommons.fiu.edu/etd/2299 ; 10.25148/etd.FIDC000196 ; FIDC000196

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