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Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks.
Lemeng Wu, Bo
Liu, Peter Stone, and Qiang Liu.
In Advances in Neural Information
Processing Systems 34 (2020), December 2020.
[PDF]8.1MB [slides.pdf]744.8kB
We propose firefly neural architecture descent, a general framework for progressively and dynamically growing neural networks to jointly optimize the networks’ parameters and architectures. Our method works in a steepest descent fashion, which iteratively finds the best network within a functional neighborhood of the original network that includes a diverse set of candidate network structures. By using Taylor approximation, the optimal network structure in the neighborhood can be found with a greedy selection procedure. We show that firefly descent can flexibly grow networks both wider and deeper, and can be applied to learn accurate but resource-efficient neural architectures that avoid catastrophic forgetting in continual learning. Empirically, firefly descent achieves promising results on both neural architecture search and continual learning. In particular, on a challenging continual image classification task, it learns networks that are smaller in size but have higher average accuracy than those learned by the state-of-the-art methods.
@InProceedings{NeurIPS2020-Wu,
author = {Lemeng Wu and Bo Liu and Peter Stone and Qiang Liu},
title = {Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks},
booktitle = {Advances in Neural Information Processing Systems 34 (2020)},
location = {Vancouver, Canada},
month = {December},
year = {2020},
abstract = {
We propose firefly neural architecture descent, a general framework for
progressively and dynamically growing neural networks to jointly optimize the
networks’ parameters and architectures. Our method works in a steepest descent
fashion, which iteratively finds the best network within a functional
neighborhood of the original network that includes a diverse set of candidate
network structures. By using Taylor approximation, the optimal network structure
in the neighborhood can be found with a greedy selection procedure. We show that
firefly descent can flexibly grow networks both wider and deeper, and can be
applied to learn accurate but resource-efficient neural architectures that avoid
catastrophic forgetting in continual learning. Empirically, firefly descent
achieves promising results on both neural architecture search and continual
learning. In particular, on a challenging continual image classification task,
it learns networks that are smaller in size but have higher average accuracy
than those learned by the state-of-the-art methods.
},
}
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