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Quadratic Suffices for Over-parametrization via Matrix Chernoff Bound

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arxiv 1906.03593 v2 pith:BUY6ZYX3 submitted 2019-06-09 cs.LG cs.AIstat.ML

Zhao Song , Xin Yang This is my paper · ORCID
classification cs.LGcs.AIstat.ML
keywords over-parametrizationbeautifulboundchernoffdeepimprovelearningliang
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We improve the over-parametrization size over two beautiful results [Li and Liang' 2018] and [Du, Zhai, Poczos and Singh' 2019] in deep learning theory.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes

    stat.ML 2019-08 conditional novelty 6.0 of 10

    Gradient descent on a network with a final hidden layer of width O(n) can interpolate any n-point dataset and reach a global optimum, and this linear rate is optimal.

  2. Numerical Pruning for Efficient Autoregressive Models

    cs.LG 2024-12 conditional novelty 5.0 of 10

    A Newton's method derived importance score plus a constrained least-squares compensation prunes LLaMA and LlamaGen models with reported gains over LLM-Pruner, SliceGPT, and FLAP.

  3. Unifying Learning Dynamics and Generalization in Transformers Scaling Law

    cs.LG 2025-12 reject novelty 4.0 of 10

    Claims a two-stage transformer scaling law (exponential then C^{-1/6}) with matching bounds, but the lower bounds are missing, the exponent is inconsistent (-1/7 vs -1/6), and the law is an artifact of hand-set M = Θ(...

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