`

Timezone: »

 
Simulated Annealing for Neural Architecture Search
Shentong Mo · Jingfei Xia · Pinxu Ren

Gradient-based Neural Architecture Search (NAS) approaches have achieved remarkable progress in the automated machine learning community. However, previous methods would cause much search time and huge computation resources in a big search space for seeking an optimal network structure. In this work, we propose a novel Simulated Annealing algorithm for NAS, namely SA-NAS, by adding perturbations to the gradient-descent for saving search cost and boosting the predictive performance of the search architecture. Our proposed algorithm is easy to be adapted to current state-of-the-art methods in the literature. We conduct extensive experiments on various benchmarks where the results demonstrate the effectiveness and efficiency of our SA-NAS in reducing search time and saving computation resources. Compared to previous differentiable methods, our SA-NAS achieves comparable or better predictive performance under the same setting.

Author Information

Shentong Mo (CMU)
Jingfei Xia (Carnegie Mellon University)
Pinxu Ren (Carnegie Mellon University)

More from the Same Authors