covariance recovery 1deep generative models 1model evaluation 1non-stationary gaussian random fields 1spatial statistics 1
From the 1 of 2 linked papers with an AI index.
2 papers
stat.ML2026
Can Deep Generative Models Reproduce Non-Stationary Gaussian Random Fields?
Daniel Kua, Yan Song
The paper evaluates several deep generative models on a synthetic non‑stationary Gaussian random field to see how well they recover the true mean and covariance, and demonstrates t…
cs.SD2026
Enhancing Flow Matching with A Unified Guidance Framework for Efficient and Robust Speech Synthesis
Zuda Yu, Qianhui Xu, Ting Chen +5
Flow Matching (FM) has emerged as a powerful paradigm for speech generation but remains constrained by high inference latency and timbre leakage. To address these bottlenecks, we p…