mlsanityData to 14 Oct 2026

Alternators With Noise Models

arXiv 2505.12544 ↗First posted 18 May 2025cs.LGcited by 0

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Alternators have recently been introduced as a framework for modeling time-dependent data. They often outperform other popular frameworks, such as state-space models and diffusion models, on challenging time-series tasks. This paper introduces a new Alternator model, called Alternator++, which enhances the flexibility of traditional Alternators by explicitly modeling the noise terms used to sample the latent and observed trajectories, drawing on the idea of noise models from the diffusion modeling literature. Alternator++ optimizes the sum of the Alternator loss and a noise-matching loss. The latter forces the noise trajectories generated by the two noise models to approximate the noise trajectories that produce the observed and latent trajectories. We demonstrate the effectiveness of Alternator++ in tasks such as density estimation, time series imputation, and forecasting, showing that it outperforms several strong baselines, including Mambas, ScoreGrad, and Dyffusion.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 48 of its 61 references that are in the map. First posted on arXiv 18 May 2025.

Builds on

48 of 61 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024ANT: Adaptive Noise Schedule for Time Series Diffusion Models18 Oct
  2. 2023Non-autoregressive Conditional Diffusion Models for Time Series Prediction8 Jun
  3. 2023DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting3 Jun
  4. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation7 Jul
  5. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting28 Jan

Other references · 43

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  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  5. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
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  12. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,778
31 more
  1. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,354
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  3. 2022Simplified State Space Layers for Sequence Modelingcited by 788
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