A Study of Posterior Stability for Time-Series Latent Diffusion

• URL: http://arxiv.org/abs/2405.14021v2
• Date: Wed, 02 Oct 2024 18:40:47 GMT
• Title: A Study of Posterior Stability for Time-Series Latent Diffusion
• Authors: Yangming Li, Yixin Cheng, Mihaela van der Schaar,
• Abstract summary: We first show that posterior collapse will reduce latent diffusion to a variational autoencoder (VAE), making it less expressive. We then introduce a principled method: dependency measure, that quantifies the sensitivity of a recurrent decoder to input variables. Building on our theoretical and empirical studies, we introduce a new framework that extends latent diffusion and has a stable posterior.
• Score: 59.41969496514184
• License:
• Abstract: Latent diffusion has demonstrated promising results in image generation and permits efficient sampling. However, this framework might suffer from the problem of posterior collapse when applied to time series. In this paper, we first show that posterior collapse will reduce latent diffusion to a variational autoencoder (VAE), making it less expressive. This highlights the importance of addressing this issue. We then introduce a principled method: dependency measure, that quantifies the sensitivity of a recurrent decoder to input variables. Using this tool, we confirm that posterior collapse significantly affects time-series latent diffusion on real datasets, and a phenomenon termed dependency illusion is also discovered in the case of shuffled time series. Finally, building on our theoretical and empirical studies, we introduce a new framework that extends latent diffusion and has a stable posterior. Extensive experiments on multiple real time-series datasets show that our new framework is free from posterior collapse and significantly outperforms previous baselines in time series synthesis.

Related papers

• Retrieval-Augmented Diffusion Models for Time Series Forecasting [19.251274915003265]
  We propose a Retrieval- Augmented Time series Diffusion model (RATD) RATD consists of two parts: an embedding-based retrieval process and a reference-guided diffusion model. Our approach allows leveraging meaningful samples within the database to aid in sampling, thus maximizing the utilization of datasets.
  arXiv  Detail & Related papers  (2024-10-24T13:14:39Z)
• Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks [12.784885649573994]
  Causal discovery from time-series data aims to capture both intra-slice (contemporaneous) and inter-slice (time-lagged) causality. We propose a novel gradient-based causal discovery approach STIC, which focuses on textbfShort-textbfTerm textbfInvariance using textbfConvolutional neural networks.
  arXiv  Detail & Related papers  (2024-08-15T08:43:28Z)
• Diffusion-TS: Interpretable Diffusion for General Time Series Generation [6.639630994040322]
  Diffusion-TS is a novel diffusion-based framework that generates time series samples of high quality. We train the model to directly reconstruct the sample instead of the noise in each diffusion step, combining a Fourier-based loss term. Results show that Diffusion-TS achieves the state-of-the-art results on various realistic analyses of time series.
  arXiv  Detail & Related papers  (2024-03-04T05:39:23Z)
• Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation [53.27596811146316]
  Diffusion models operate over a sequence of timesteps instead of instantaneous input-output relationships in previous contexts. We present Diffusion-TracIn that incorporates this temporal dynamics and observe that samples' loss gradient norms are highly dependent on timestep. We introduce Diffusion-ReTrac as a re-normalized adaptation that enables the retrieval of training samples more targeted to the test sample of interest.
  arXiv  Detail & Related papers  (2024-01-17T07:58:18Z)
• Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models [72.07462371883501]
  We propose emphProjection Regret (PR), an efficient novelty detection method that mitigates the bias of non-semantic information. PR computes the perceptual distance between the test image and its diffusion-based projection to detect abnormality. Extensive experiments demonstrate that PR outperforms the prior art of generative-model-based novelty detection methods by a significant margin.
  arXiv  Detail & Related papers  (2023-12-05T09:44:47Z)
• SWoTTeD: An Extension of Tensor Decomposition to Temporal Phenotyping [0.0]
  We propose SWoTTeD (Sliding Window for Temporal Decomposition), a novel method to discover hidden temporal patterns. We validate our proposal using both synthetic and real-world datasets, and we present an original usecase using data from the Greater Paris University Hospital. The results show that SWoTTeD achieves at least as accurate reconstruction as recent state-of-the-art tensor decomposition models.
  arXiv  Detail & Related papers  (2023-10-02T13:42:11Z)
• Reconstructing Graph Diffusion History from a Single Snapshot [87.20550495678907]
  We propose a novel barycenter formulation for reconstructing Diffusion history from A single SnapsHot (DASH) We prove that estimation error of diffusion parameters is unavoidable due to NP-hardness of diffusion parameter estimation. We also develop an effective solver named DIffusion hiTting Times with Optimal proposal (DITTO)
  arXiv  Detail & Related papers  (2023-06-01T09:39:32Z)
• ChiroDiff: Modelling chirographic data with Diffusion Models [132.5223191478268]
  We introduce a powerful model-class namely "Denoising Diffusion Probabilistic Models" or DDPMs for chirographic data. Our model named "ChiroDiff", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate.
  arXiv  Detail & Related papers  (2023-04-07T15:17:48Z)
• Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement [51.55157852647306]
  Time series forecasting has been a widely explored task of great importance in many applications. It is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. We propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder equipped with diffusion, denoise, and disentanglement.
  arXiv  Detail & Related papers  (2023-01-08T12:20:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.