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Found 3 papers, 2 papers with code
Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search
In the past few years, Differentiable Neural Architecture Search (DNAS) rapidly imposed itself as the trending approach to automate the discovery of deep neural network architectures.
NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks
In this article, we present NASiam, a novel approach that uses for the first time differentiable NAS to improve the multilayer perceptron projector and predictor (encoder/predictor pair) architectures inside siamese-networks-based contrastive learning frameworks (e. g., SimCLR, SimSiam, and MoCo) while preserving the simplicity of previous baselines.
D-DARTS: Distributed Differentiable Architecture Search
This approach is accompanied by a novel metric that measures the distance between architectures inside our custom search space.
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