11 papers
Quantization-Aware Kalman Estimation for Diffusion Sampling
Qitan Shi, Cheng Jin, Jiawei Zhang +1
Quantization offers a practical path to deploying diffusion models with reduced memory and computation, but aggressive compression can cause quantized outputs to deviate substantia…
Manifold-Constrained Noise Optimization for Diverse Diffusion Sampling
Qitan Shi, Cheng Jin, Ziyuan Liu +1
Few-step distilled diffusion models generate high-quality images quickly, but often lose per-prompt diversity, producing near-identical samples across random seeds. Optimizing the…
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…
EPA: Neural Collapse Inspired Robust Out-of-Distribution Detector
Jiawei Zhang, Yufan Chen, Cheng Jin +2
Out-of-distribution (OOD) detection plays a crucial role in ensuring the security of neural networks. Existing works have leveraged the fact that In-distribution (ID) samples form…
ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction
Zhilei Hu, Zixuan Li, Daozhu Xu +5
Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes,…
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data
Xiaolu Wang, Cheng Jin, Hoi-To Wai +1
This paper considers a type of incremental aggregated gradient (IAG) method for large-scale distributed optimization. The IAG method is well suited for the parameter server archite…