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information theory

topic3 events
papersTODAY 04:00 UTC

WaterKron Method Ties Kronecker-Factored Hessian Choice to Information Theory for Quantization

A new preprint introduces WaterKron, a post-training quantization approach that pairs two-sided GPTQ with waterfilling-based scaling that varies by row and column, along with entropy coding. The authors also present FlipFlop Hessian, a way of selecting Kronecker-factored Hessian approximations that they ground in information-theoretic arguments. The work targets how such approximations should be chosen when compressing neural networks after training.

quantization/llm-inference/arxiv/compression
papersTODAY 04:00 UTC

Paper Links Zero-SNR Analyticity of Scalar MMSE to Gaussian Inputs

The paper studies a scalar channel where a real random variable X is observed under additive standard Gaussian noise scaled by the square root of a signal-to-noise parameter. Assuming a square-exponential moment condition on X, the author shows that the minimum mean-square error behaves analytically near zero SNR only when X is Gaussian, making that property equivalent to Gaussianity of the input. The result gives a way to detect non-Gaussian structure from the low-SNR behavior of the MMSE curve.

information-theory/estimation/gaussian/arxiv
papersSEP 12 04:00 UTC

Information-Theoretic Framework Unifies Generalization Bounds for VAEs and Diffusion Models

A new arXiv paper derives generalization guarantees for both variational autoencoders and diffusion models within a single information-theoretic framework. The analysis exploits the encoder-generator structure shared by the two model families, which earlier theoretical work had largely treated separately. The authors report bounds that clarify how the shared architecture affects performance on unseen data.

research/diffusion/generative-models/theory