ReLU networks approximate traceable definable subsets of the unit cube in L^p with size O(ε^{-p(n-1)/m}) and yield ERM learning rates of order N^{-m/(m+pn-p)} for hinge loss under uniform component bounds.
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4 Pith papers cite this work. Polarity classification is still indexing.
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The Multi-Block DC class admits polynomial-size DC decompositions for problems that require exponential size under standard DC programming and supplies explicit constructive formulations for deep ReLU networks together with convergent batch and stochastic algorithms.
A PnP framework with axial coupling and Woodbury updates recovers cellular structures from compressed CS-LSM measurements of zebrafish hearts under a weakly convex regularization assumption.
Risk-sensitive preference games using convex risk measures produce policies that are robust across data strata and match or exceed standard Nash learning performance without added cost.
citing papers explorer
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Fast approximation and learning of binary classification tasks in o-minimal structures using ReLU neural networks
ReLU networks approximate traceable definable subsets of the unit cube in L^p with size O(ε^{-p(n-1)/m}) and yield ERM learning rates of order N^{-m/(m+pn-p)} for hinge loss under uniform component bounds.
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The Multi-Block DC Function Class: Theory, Algorithms, and Applications
The Multi-Block DC class admits polynomial-size DC decompositions for problems that require exponential size under standard DC programming and supplies explicit constructive formulations for deep ReLU networks together with convergent batch and stochastic algorithms.
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Plug-and-Play Volumetric Reconstruction for Compressive Sensing Light-Sheet Microscopy
A PnP framework with axial coupling and Woodbury updates recovers cellular structures from compressed CS-LSM measurements of zebrafish hearts under a weakly convex regularization assumption.
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Structure from Strategic Interaction & Uncertainty: Risk Sensitive Games for Robust Preference Learning
Risk-sensitive preference games using convex risk measures produce policies that are robust across data strata and match or exceed standard Nash learning performance without added cost.