Variance misspecification in Gaussian mixtures creates a phase diagram: correct specification recovers true means independent of SNR, under-smoothing biases means with SNR^{-1} error in low SNR, and over-smoothing collapses clusters above an SNR-dependent threshold.
Least squares quantization in PCM.IEEE Transactions on Information Theory, 28(2):129–137
3 Pith papers cite this work. Polarity classification is still indexing.
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OCTOPUS compresses KV caches via octahedral parametrization of rotated triplets and squared-error-optimized Lloyd-Max quantization, matching or exceeding prior rotation codecs with growing gains at low bit widths.
Contextual Plackett-Luce extends the classical Plackett-Luce model with context-dependent Ising parameterization to enable efficient parallel scoring followed by incremental autoregressive selection for ambiguous sequence tasks.
citing papers explorer
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The interplay of signal-to-noise ratio and variance misspecification in Gaussian mixtures
Variance misspecification in Gaussian mixtures creates a phase diagram: correct specification recovers true means independent of SNR, under-smoothing biases means with SNR^{-1} error in low SNR, and over-smoothing collapses clusters above an SNR-dependent threshold.
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OCTOPUS: Optimized KV Cache for Transformers via Octahedral Parametrization Under optimal Squared error quantization
OCTOPUS compresses KV caches via octahedral parametrization of rotated triplets and squared-error-optimized Lloyd-Max quantization, matching or exceeding prior rotation codecs with growing gains at low bit widths.
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Contextual Plackett-Luce: An Efficient Neural Model for Probabilistic Sequence Selection under Ambiguity
Contextual Plackett-Luce extends the classical Plackett-Luce model with context-dependent Ising parameterization to enable efficient parallel scoring followed by incremental autoregressive selection for ambiguous sequence tasks.