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Neural network flatness linked to generalization in new study

By PulseAugur Editorial · [3 sources] ·

A new research paper explores the relationship between model flatness and generalization in neural networks. Despite prior work suggesting symmetries render flatness a vacuous metric, this study demonstrates a connection for learning multi-index models with homogeneous neural networks. The research identifies specific classes of non-generalizing interpolators and proves that the "flattest" interpolators achieve low population loss, establishing a direct link between flatness and generalization across various activations and data distributions. AI

IMPACT Establishes a theoretical link between model flatness and generalization, potentially guiding future research in neural network optimization and design.

RANK_REASON The cluster contains an academic paper discussing theoretical aspects of neural network generalization.

Read on arXiv stat.ML →

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

Neural network flatness linked to generalization in new study

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COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Flatness and Generalization: Learning Multi-Index Models with Homogeneous Neural Networks

    A common heuristic used to explain the generalization of first-order gradient methods on non-convex neural networks is that "flat interpolators generalize well" (Hochreiter and Schmidhuber, 1994; Keskar et al., 2017), where flatness can be measured by the trace of the Hessian of …

  2. arXiv stat.ML TIER_1 English(EN) · Harsh Vardhan, Hossein Taheri, Arya Mazumdar ·

    Flatness and Generalization: Learning Multi-Index Models with Homogeneous Neural Networks

    arXiv:2606.04429v1 Announce Type: new Abstract: A common heuristic used to explain the generalization of first-order gradient methods on non-convex neural networks is that "flat interpolators generalize well" (Hochreiter and Schmidhuber, 1994; Keskar et al., 2017), where flatness…

  3. arXiv stat.ML TIER_1 English(EN) · Arya Mazumdar ·

    Flatness and Generalization: Learning Multi-Index Models with Homogeneous Neural Networks

    A common heuristic used to explain the generalization of first-order gradient methods on non-convex neural networks is that "flat interpolators generalize well" (Hochreiter and Schmidhuber, 1994; Keskar et al., 2017), where flatness can be measured by the trace of the Hessian of …

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