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Poster
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks
Lemeng Wu · Bo Liu · Peter Stone · Qiang Liu

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #295

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.

Author Information

Lemeng Wu (UT Austin)
Bo Liu (University of Texas at Austin)
Peter Stone (The University of Texas at Austin, Sony AI)
Qiang Liu (UT Austin)

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