KME-CLIP is proposed, which leverages this structure through the inner product in a reproducing kernel Hilbert space (RKHS) and theoretically proves that the KME-CLIP similarity can bring the contrastive loss arbitrarily close to its optimal value, which is attained by PMI, as the size of the point set grows.
This paper theoretically analyzes the MaskGIT sampler for image modeling, revealing its implicit temperature sampling mechanism, and shows that MaskGIT is asymptotically equivalent to a choose-then-sample (CTS) formulation, instantiated as the "moment sampler," which explicitly separates index selection from token samp...
It is shown that the population-optimal UDM predictor generally depends on time: time controls how much the model should trust the observed context, and this dependence can become negligible in finite-data settings relevant to language.
Chunsan Hong, Chieh-Hsin Lai, Satoshi Hayakawa et al.· 0 citations
Spectral Alignment is proposed, a lightweight, guidance-based method that calibrates the power spectrum of intermediate predictions to a pre-computed prior and is complementary to Classifier-Free Guidance (CFG).
A general statistical framework for studying asymptotically optimal noise-level allocation in diffusion training and an idealized independent-learner regime, intended to model temporal specialization in neural networks are developed.
Luca Ambrogioni, Giulio Franzese, Alberto Foresti et al.· 2 citations
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