Difference between revisions of "Neural Architecture"

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* [http://www.automl.org/automl/literature-on-neural-architecture-search/ Literature on Neural Architecture Search |] [http://www.automl.org/ AutoML.org]
 
* [http://www.automl.org/automl/literature-on-neural-architecture-search/ Literature on Neural Architecture Search |] [http://www.automl.org/ AutoML.org]
* [[Automated Machine Learning (AML) - AutoML]]
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* [[Auto Keras]]
 
* [[Evolutionary Computation / Genetic Algorithms]]
 
* [[Hyperparameter]]s Optimization
 
 
* [http://en.wikipedia.org/wiki/Neural_architecture_search#NAS_with_Reinforcement_Learning Neural Architecture Search (NAS) with Reinforcement Learning | Wikipedia]
 
* [http://en.wikipedia.org/wiki/Neural_architecture_search#NAS_with_Reinforcement_Learning Neural Architecture Search (NAS) with Reinforcement Learning | Wikipedia]
 
** [[Reinforcement Learning (RL)]]
 
** [[Reinforcement Learning (RL)]]
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<youtube>CYUpDogeIL0</youtube>
 
<youtube>CYUpDogeIL0</youtube>
 
<youtube>MtuADH7kVeQ</youtube>
 
<youtube>MtuADH7kVeQ</youtube>
 
  
 
= Differentiable Neural Computer (DNC) =
 
= Differentiable Neural Computer (DNC) =

Revision as of 23:11, 7 April 2020

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Neural Architecture Search (NAS)

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Various approaches to NAS have designed networks that are on par or even outperform hand-designed architectures. Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used:

  • The search space defines which type of ANN can be designed and optimized in principle.
  • The search strategy defines which strategy is used to find optimal ANN's within the search space.
  • Obtaining the performance of an ANN is costly as this requires training the ANN first. Therefore, performance estimation strategies are used obtain less costly estimates of a model's performance. Neural Architecture Search | Wikipedia

Differentiable Neural Computer (DNC)

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