Unverified50% confidenceFactExact time
The conclusion of overcoming the curse of dimensionality relies on the conditional distribution having a certain smoothness structure (such as Hölder smoothness) and on a refined characterization of the relationship between network depth, width, and the smoothness of the target function.
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10/14/2026
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Related Claims
UnverifiedThe paper proves that, under appropriate network depth and width configurations, the convergence rate of dense ReLU networks can overcome the curse of dimensionality, achieving polynomial-level sample complexity.78% similarUnverifiedAnchored Langevin dynamics decomposes the target distribution into a tractable reference distribution (smooth anchoring potential) and a residual that captures the complexity of the original target, with the algorithm taking gradients only with respect to the smooth reference potential.69% similarUnverifiedElastic-Net has a higher computational cost than single regularization methods, but in return yields more robust feature selection results.69% similarUnverifiedResearchers found that predictive metrics and correlation metrics are affected by the regularity of the observation grid and the smoothness of trajectories, such that the more regular the data and the smoother the trajectories, the better certain metrics appear.68% similarUnverifiedThe advantage of a dense architecture lies in more predictable behavior, more straightforward fine-tuning, and easier domain adaptation to achieve stable performance, at the cost of a relatively fixed inference computation load that cannot save computing power through sparse activation.68% similar
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