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Neural Spectral Capacity ranks architectures beyond parameter and FLOP counts

huggingface.co by Chenyu Zhu et al. Editor score 8/10

NSC estimates architectural capacity from weight-matrix spectra, and its dynamic-programming solver finds a budget-constrained design without instantiating a model; tests span seven Transformer and CNN families. It offers a measurable alternative to size-only proxies when choosing architectures.

What: Neural Spectral Capacity (NSC) scores neural architectures using the singular-value spectra of their weight matrices.
Method: Under standard random initialization, NSC can be computed from an architecture specification alone; NSC-DP uses dynamic programming to maximize it under resource constraints.
Why it matters: The method offers engineers an architecture-ranking and optimization approach that does not require model instantiation, data, or gradients.

NSC is computed from an architecture specification, then NSC-DP optimizes it under resource constraints. Sketched from the article's own text.

Read on huggingface.co → Code · 5 stars

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