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Spectral Architecture Search for Neural Network Models

Created by
  • Haebom

Author

Gianluca Peri, Lorenzo Chicchi, Duccio Fanelli, Lorenzo Giambagli

Outline

SPARCS (SPectral ARchiteCture Search) is a novel architecture search protocol for solving architecture design and optimization problems in artificial neural networks. It leverages the spectral properties of the interlayer transfer matrix to generate continuous and differentiable manifolds, enabling the use of gradient-based optimization algorithms. Using a simple benchmark model, we demonstrate that the proposed method generates self-emergent architectures with minimal expressive power and a reduced number of parameters compared to other feasible alternatives for the task under study.

Takeaways, Limitations

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Takeaways:
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We present a novel method for efficiently exploring neural network architectures using gradient-based optimization.
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Presenting the possibility of automatically generating architectures with only the expressive power necessary to perform a task with minimal parameters.
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Suggests the possibility of improving computational efficiency through a reduced number of parameters compared to existing methods.
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Limitations:
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The performance of the proposed method is limited to simple benchmark models, and its generalization performance on real-world complex problems requires further verification.
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Lack of clear explanation of quantitative measurement and evaluation criteria for "minimal degree of expressivity."
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Lack of comparative analysis with other state-of-the-art architecture exploration methods.
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