Dithered quantization after a single randomized Hadamard transform yields unbiased estimates whose MSE asymptotically equals that of dense random rotations, specifically (π√3/2 + o(1))·4^{-b} for b-bit TurboQuant.
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The Fréchet derivative of rank-truncated CUR is a sampling-induced oblique tangent projector, so perturbations in its kernel are removed to first order.
In the high-dimensional proportional regime, a large gradient step on a two-layer network induces a target-dependent spiked Gaussian covariance on the features, yielding a data-adaptive kernel that amplifies target-aligned eigenvalues and mixes leading eigenfunctions.
A parametric autoencoder with non-negativity and softmax constraints learns interpretable latent chemical components and couples them to kinetics and heat release for improved reduced-order modeling of decomposition.
Transfer learning from informative source networks improves target DCMM estimation accuracy by enlarging the eigenvalue gap of the connection probability matrix, with algorithms to avoid negative transfer.
RL4RLA is a reinforcement learning framework that discovers interpretable symbolic randomized linear algebra algorithms by combining curriculum learning and graph-based search to overcome sparse rewards and large search spaces.
citing papers explorer
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Provable Quantization with Randomized Hadamard Transform
Dithered quantization after a single randomized Hadamard transform yields unbiased estimates whose MSE asymptotically equals that of dense random rotations, specifically (π√3/2 + o(1))·4^{-b} for b-bit TurboQuant.
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Revisiting CUR Perturbation Analysis: A Local Tangent-Space Expansion
The Fréchet derivative of rank-truncated CUR is a sampling-induced oblique tangent projector, so perturbations in its kernel are removed to first order.
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How does feature learning reshape the function space?
In the high-dimensional proportional regime, a large gradient step on a two-layer network induces a target-dependent spiked Gaussian covariance on the features, yielding a data-adaptive kernel that amplifies target-aligned eigenvalues and mixes leading eigenfunctions.
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A Data-Driven Parametric Reduced-Order Chemical Kinetics Model Derived from Atomistic Simulations
A parametric autoencoder with non-negativity and softmax constraints learns interpretable latent chemical components and couples them to kinetics and heat release for improved reduced-order modeling of decomposition.
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Transfer Learning for Degree-Corrected Mixed Membership Network Models
Transfer learning from informative source networks improves target DCMM estimation accuracy by enlarging the eigenvalue gap of the connection probability matrix, with algorithms to avoid negative transfer.
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RL4RLA: Teaching ML to Discover Randomized Linear Algebra Algorithms Through Curriculum Design and Graph-Based Search
RL4RLA is a reinforcement learning framework that discovers interpretable symbolic randomized linear algebra algorithms by combining curriculum learning and graph-based search to overcome sparse rewards and large search spaces.