NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm

This paper introduces NSGA-Net -- an evolutionary approach for neural architecture search (NAS). NSGA-Net is designed with three goals in mind: (1) a procedure considering multiple and conflicting objectives, (2) an efficient procedure balancing exploration and exploitation of the space of potential neural network architectures, and (3) a procedure finding a diverse set of trade-off network architectures achieved in a single run... (read more)

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Methods used in the Paper


METHOD TYPE
Sigmoid Activation
Activation Functions
Tanh Activation
Activation Functions
Entropy Regularization
Regularization
PPO
Policy Gradient Methods
Softmax
Output Functions
LSTM
Recurrent Neural Networks
Neural Architecture Search
Neural Architecture Search