Efficient Automatic Optimization of Neural Network Architecture.

Date of Submission

December 2020

Date of Award

Winter 12-12-2021

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Machine Intelligence Unit (MIU-Kolkata)


Ghosh, Ashish (MIU-Kolkata; ISI)

Abstract (Summary of the Work)

Neural Networks are at the heart of deep Learning Frame works which have yielded excellent results in various complex problem domains. But the design of neural network architecture is a challenging task. Judicious selection of network architecture and manual tuning of network parameters is a tedious and time consuming process. There has been a substantial effort to automate the process of neural network design using various heuristic algorithms. Evolutionary algorithm are amongst the most successful methods to automate the network architecture search process. But these algorithms are very computation intensive. Thus we try to explore a technique that could lead to faster evolutionary algorithms to nd optimal neural network architecture. We also do a survey of various alternative methods.


ProQuest Collection ID: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:28842692

Control Number


Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.



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