Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians

Hyperparameter optimization of neural networks can be elegantly formulated as a bilevel optimization problem. While research on bilevel optimization of neural networks has been dominated by implicit differentiation and unrolling, hypernetworks such as Self-Tuning Networks (STNs) have recently gained traction due to their ability to amortize the optimization of the inner objective... (read more)

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Methods used in the Paper


METHOD TYPE
DropConnect
Regularization
Dropout
Regularization
Random Search
Hyperparameter Search
HyperNetwork
Feedforward Networks
Cutout
Image Data Augmentation