12 papers
Ctrl-Z Sampling: Scaling Diffusion Sampling with Controlled Random Zigzag Explorations
Shunqi Mao, Wei Guo, Chaoyi Zhang +3
Diffusion models generate conditional samples by progressively denoising Gaussian noise, yet the denoising trajectory can stall at visually plausible but low-quality outcomes with…
MetaDNS: Enhancing Exploration in Discrete Neural Samplers via Well-Tempered Metadynamics
Xiaochen Du, Juno Nam, Jaemoo Choi +7
Sampling from discrete distributions with multiple modes and energy barriers is fundamental to machine learning and computational physics. Recent discrete neural samplers like MDNS…
Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
Jaemoo Choi, Yuchen Zhu, Wei Guo +6
Reinforcement learning has been widely applied to diffusion and flow models for visual tasks such as text-to-image generation. However, these tasks remain challenging because diffu…
Proximal Diffusion Neural Sampler
Wei Guo, Jaemoo Choi, Yuchen Zhu +2
The task of learning a diffusion-based neural sampler for drawing samples from an unnormalized target distribution can be viewed as a stochastic optimal control problem on path mea…
Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond
Wei Guo, Molei Tao, Yongxin Chen
Given an unnormalized probability density , estimating its normalizing constant or free energy $F=-\l…
Continuous Diffusion Scales Competitively with Discrete Diffusion for Language
Zhihan Yang, Wei Guo, Shuibai Zhang +5
While diffusion has drawn considerable recent attention from the language modeling community, continuous diffusion has appeared less scalable than discrete approaches. To challenge…