7 papers · 1 filter
Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models
Musheng Li, Ziying Zhang, Cheng jin +1
Token-based time series large language models (TS-LLMs) have emerged as a promising direction for time series analysis and reasoning. However, prior studies largely overlook the in…
Stage-wise Dynamics of Classifier-Free Guidance in Diffusion Models
Cheng Jin, Qitan Shi, Yuantao Gu
Classifier-Free Guidance (CFG) is widely used to improve conditional fidelity in diffusion models, but its impact on sampling dynamics remains poorly understood. Prior studies, oft…
ReTrack: Data Unlearning in Diffusion Models through Redirecting the Denoising Trajectory
Qitan Shi, Cheng Jin, Jiawei Zhang +1
Diffusion models excel at generating high-quality, diverse images but suffer from training data memorization, raising critical privacy and safety concerns. Data unlearning has emer…
A-FloPS: Accelerating Diffusion Models via Adaptive Flow Path Sampler
Cheng Jin, Zhenyu Xiao, Yuantao Gu
Diffusion models deliver state-of-the-art generative performance across diverse modalities but remain computationally expensive due to their inherently iterative sampling process.…
Angle Domain Guidance: Latent Diffusion Requires Rotation Rather Than Extrapolation
Cheng Jin, Zhenyu Xiao, Chutao Liu +1
Classifier-free guidance (CFG) has emerged as a pivotal advancement in text-to-image latent diffusion models, establishing itself as a cornerstone technique for achieving high-qual…
Unleashing the Denoising Capability of Diffusion Prior for Solving Inverse Problems
Jiawei Zhang, Jiaxin Zhuang, Cheng Jin +2
The recent emergence of diffusion models has significantly advanced the precision of learnable priors, presenting innovative avenues for addressing inverse problems. Since inverse…