English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In Diffusion Probabilistic Models (DPMs), the task of modeling the scoreevolution via a single time-dependent neural network necessitates extendedtraining periods and may potentially impede modeling flexibility and capacity.To counteract these challenges, we propose leveraging the independence oflearning tasks at different time points inherent to DPMs. More specifically, wepartition the learning task by utilizing independent networks, each dedicatedto learning the evolution of scores within a specific time sub-interval.Further, inspired by residual flows, we extend this strategy to its logicalconclusion by employing separate networks to independently model the score ateach individual time point. As empirically demonstrated on synthetic and imagedatasets, our approach not only significantly accelerates the training processby introducing an additional layer of parallelization atop dataparallelization, but it also enhances density estimation performance whencompared to the conventional training methodology for DPMs.

Faster Training of Diffusion Models and Improved Density Estimation via Parallel Score Matching