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Neural network capacity analyzed using HCIZ formula

By PulseAugur Editorial · [1 sources] ·

Researchers have analyzed the storage capacities of neural networks when real pre-activations are enforced within complex hypothesis classes. Their work utilizes the Harish-Chandra-Itzykson-Zuber (HCIZ) formula, a method not commonly found in this field, to derive a more stable approximation for the asymptotic ratio. This approach is specifically tailored for their research by integrating over unitary and orthogonal compact manifolds, employing the Weyl integration formula and the Haar measure. AI

RANK_REASON The cluster contains a research paper detailing theoretical analysis of neural network properties. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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Neural network capacity analyzed using HCIZ formula

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The cluster contains a research paper detailing theoretical analysis of neural network properties. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Andrew Gracyk ·

    Shortcomings and capacities of real-constrained neural networks in complex spaces

    arXiv:2606.04390v1 Announce Type: new Abstract: We find the asymptotic ratio between the storage capacities when enforcing real pre-activations in a complex hypothesis class as opposed to complex ones in the same class. Our methods depend on Gardner volume comparisons at critical…

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