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New framework unifies representation costs for deep neural networks

By PulseAugur Editorial · [3 sources] ·

A new research paper introduces a unified framework for analyzing the representation costs of parametric data-fitting methods. This framework reveals the induced function spaces for various models, including kernel methods, wavelets, and shallow neural networks, as special cases. For deep neural networks with ReLU activations, the paper demonstrates that their native spaces are quasi-Banach spaces where the inductive bias cannot be captured by norms for depths greater than two. AI

IMPACT This research provides a theoretical foundation for understanding the inductive biases of deep neural networks, potentially guiding future model design.

RANK_REASON The cluster contains a research paper published on arXiv detailing new theoretical frameworks for understanding neural networks.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New framework unifies representation costs for deep neural networks

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The cluster contains a research paper published on arXiv detailing new theoretical frameworks for understanding neural networks.
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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Greg Ongie, Rahul Parhi ·

    Representation Costs in Data Science: Foundations and the Quasi-Banach Spaces of Deep Neural Networks

    arXiv:2606.14954v1 Announce Type: cross Abstract: We develop a general framework for analyzing representation costs of parametric data-fitting methods through their parameter-space regularizers. From this abstract perspective, we define representation costs for arbitrary parametr…

  2. arXiv stat.ML TIER_1 English(EN) · Rahul Parhi ·

    Representation Costs in Data Science: Foundations and the Quasi-Banach Spaces of Deep Neural Networks

    We develop a general framework for analyzing representation costs of parametric data-fitting methods through their parameter-space regularizers. From this abstract perspective, we define representation costs for arbitrary parametric models and reveal their induced (native) functi…

  3. arXiv stat.ML TIER_1 English(EN) · Rahul Parhi ·

    Representation Costs in Data Science: Foundations and the Quasi-Banach Spaces of Deep Neural Networks

    We develop a general framework for analyzing representation costs of parametric data-fitting methods through their parameter-space regularizers. From this abstract perspective, we define representation costs for arbitrary parametric models and reveal their induced (native) functi…

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