Transformers converge globally to the optimal DDPM denoiser for multi-token GMMs via self-attention mean denoising, with explicit token and iteration requirements.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
TimeTok is a unified framework using hierarchical tokenization for granularity-controllable time-series generation that achieves state-of-the-art performance in standard tasks and shows transferability across heterogeneous datasets.
citing papers explorer
-
Transformers Learn the Optimal DDPM Denoiser for Multi-Token GMMs
Transformers converge globally to the optimal DDPM denoiser for multi-token GMMs via self-attention mean denoising, with explicit token and iteration requirements.
-
TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization
TimeTok is a unified framework using hierarchical tokenization for granularity-controllable time-series generation that achieves state-of-the-art performance in standard tasks and shows transferability across heterogeneous datasets.