3 citations · 6 across the 12 of their papers we have counts for
5 papers · 1 filter
DiFA: Inference-Time Forward-Process Alignment for Diffusion Models
Shigui Li, Delu Zeng
The prevailing inference framework for diffusion models formulates generation fundamentally as a problem of numerical integration. This perspective casts the model as an exact esti…
Implicit Variational Rejection Sampling
Jian Xu, Shigui Li, Wei Chen +6
Variational Inference (VI) is a fundamental inference technique in Bayesian machine learning for approximating complex posterior distributions. Traditional VI often relies on the m…
Mitigating the Contractivity Trap in Diffusion ODEs via Stein Stabilization
Shigui Li, Delu Zeng
A fundamental tension exists in the large-step inference of diffusion models via their deterministic probability flow ordinary differential equation (PF-ODE) trajectories, which we…
Local-Time Riemannian Score Matching on the Quantum Pure-State Manifold
Jian Xu, Wei Chen, Shigui Li +4
Score-based diffusion can be defined intrinsically on the manifold of quantum pure states, with the Fubini--Study metric, but no closed-form transition density…
A Minimum Variance Path Principle for Accurate and Stable Score-Based Density Ratio Estimation
Wei Chen, Jiacheng Li, Shigui Li +4
Score-based methods are powerful across machine learning, but they face a paradox: theoretically path-independent, yet practically path-dependent. We resolve this by proving that p…