Unverified50% confidenceOpinionExact time
The paper points out that the analysis relies on moment conditions (p>1 or second moments), which heavy-tailed gradient distributions in deep learning may fail to satisfy.
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10/14/2026
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UnverifiedThe paper notes that proving strict expansion or depth separation requires introducing explicit complexity assumptions, and that under the assumption BPP=P, the randomized classes degenerate into their exact counterparts with the same frontier.74% similarUnverified无梯度方法样本效率低，在高维参数空间中缺乏梯度指引意味着需要海量函数评估才能收敛73% similarUnverifiedHigh-dimensional structure learning of Gaussian DAGs faces a statistical-computational gap: methods with sharp sample complexity rely on computationally expensive subset searches and require a pre-specified in-degree upper bound, while polynomial-time algorithms exhibit comparatively inferior sample complexity.73% similarUnverifiedDeep learning almost always faces non-convex optimization problems, where saddle points are more common than local minima in high-dimensional parameter spaces.72% similarUnverifiedThe learning procedure designed in the paper has two outputs: under the premise of a controlled relaxation of the approximation threshold, certifying that no algebraic complete intersection satisfying the conditions exists, or returning a candidate regression manifold with controlled geometric complexity.72% similar
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