LP-NAS: Linear Programming-based Neural Architecture Search
Neural Architecture Search (NAS) aims to automate neural network architecture design, reducing reliance on human expertise. Among the various NAS methods, differentiable NAS has gained prominence due to its efficiency and accuracy compared to conventional NAS approaches. Since differentiable NAS relaxes the architecture search space into a continuous domain, it is possible to apply principles from continuous optimization to NAS. In this paper, we propose Linear Programming-based NAS (LP-NAS), a mathematical programming-based framework for differentiable NAS that is applicable to a wide range o
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- LinkedLinked via arxiv author · 85%Abhishek Shukla →
“LP-NAS: Linear Programming-based Neural Architecture Search”
- LinkedLinked via arxiv author · 85%Ankur Sinha →
“LP-NAS: Linear Programming-based Neural Architecture Search”
- LinkedLinked via arxiv author · 85%Faiz Hamid →
“LP-NAS: Linear Programming-based Neural Architecture Search”