5 papers · 1 filter
Posterior Sampling by Combining Diffusion Models with Annealed Langevin Dynamics
Zhiyang Xun, Shivam Gupta, Eric Price
Given a noisy linear measurement of a distribution , and a good approximation to the prior , when can we sample from the posterior ? Posterio…
Improved Sample Complexity Bounds for Diffusion Model Training
Shivam Gupta, Aditya Parulekar, Eric Price +1
Diffusion models have become the most popular approach to deep generative modeling of images, largely due to their empirical performance and reliability. From a theoretical standpo…
Diffusion Posterior Sampling is Computationally Intractable
Shivam Gupta, Ajil Jalal, Aditya Parulekar +2
Diffusion models are a remarkably effective way of learning and sampling from a distribution . In posterior sampling, one is also given a measurement model and…
Efficient Knowledge Distillation via Curriculum Extraction
Shivam Gupta, Sushrut Karmalkar
Knowledge distillation is a technique used to train a small student network using the output generated by a large teacher network, and has many empirical advantages~\citep{Hinton20…
Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel
Shivam Gupta, Linda Cai, Sitan Chen
Sampling algorithms play an important role in controlling the quality and runtime of diffusion model inference. In recent years, a number of works~\cite{chen2023sampling,chen2023od…