mlsanityData to 14 Oct 2026

Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models

arXiv 2511.21320 ↗First posted 26 Nov 2025cs.LGcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Denoising Diffusion Probabilistic Models (DDPMs) can generate synthetic timeseries data to help improve the performance of a classifier, but their sampling process is computationally expensive. We address this by combining implicit diffusion models with a novel Sawtooth Sampler that accelerates the reverse process and can be applied to any pretrained diffusion model. Our approach achieves a 30 times speed-up over the standard baseline while also enhancing the quality of the generated sequences for classification tasks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 6 of its 7 references that are in the map. First posted on arXiv 26 Nov 2025.

Builds on

6 of 7 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Restart Sampling for Improving Generative Processes26 Jun
  2. 2020Denoising Diffusion Implicit Models6 Oct
  3. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 3

  1. 2024Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generationcited by 33
  2. 2024IMUDiffusion: A Diffusion Model for Multivariate Time Series Synthetisation for Inertial Motion Capturing Systemscited by 5
  3. 2025Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUscited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Denoising Diffusion Probabilistic Models in Six Simple Steps
  2. 2023Fast Diffusion Probabilistic Model Sampling through the lens of Backward Error Analysis
  3. 2021Learning to Efficiently Sample from Diffusion Probabilistic Models
  4. 2022Pseudo Numerical Methods for Diffusion Models on Manifolds
  5. 2022Accelerating Diffusion Models via Early Stop of the Diffusion Process
  6. 2023Denoising Diffusion Samplers
  7. 2023UDPM: Upsampling Diffusion Probabilistic Models
  8. 2024Diffusion Bridge Implicit Models