Feb. 13, 2024, 5:42 a.m. | Kushagra Pandey Maja Rudolph Stephan Mandt

cs.LG updates on arXiv.org arxiv.org

Diffusion models suffer from slow sample generation at inference time. Despite recent efforts, improving the sampling efficiency of stochastic samplers for diffusion models remains a promising direction. We propose Splitting Integrators for fast stochastic sampling in pre-trained diffusion models in augmented spaces. Commonly used in molecular dynamics, splitting-based integrators attempt to improve sampling efficiency by cleverly alternating between numerical updates involving the data, auxiliary, or noise variables. However, we show that a naive application of splitting integrators is sub-optimal for …

cs.lg diffusion diffusion models dynamics efficiency generative generative models inference molecular dynamics sample sampling spaces stat.ml stochastic

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Data Engineering Director-Big Data technologies (Hadoop, Spark, Hive, Kafka)

@ Visa | Bengaluru, India

Senior Data Engineer

@ Manulife | Makati City, Manulife Philippines Head Office

GDS Consulting Senior Data Scientist 2

@ EY | Taguig, PH, 1634

IT Data Analyst Team Lead

@ Rosecrance | Rockford, Illinois, United States