本文的亮点在于,diffusion model 的网络结构不再是 CSDI[2] 中的transformer 结构,而是 structured state-space model(SSM)。我们可以把这种结构理解为 RNN、一维 CNN 以及 transformer 的平替结构,都是 seq-to-seq 模型,且可以做到输入输出大小一致。本文实验显示,使用了 SSM 架构的 diffusion model 在任务上的...
Python code of the paper Model order reduction of deep structured state-space models: A system-theoretic approach machine-learning system-identification model-order-reduction linear-recurrent-units structured-state-space-models Updated Jul 25, 2024 Python ...
Structured state-space modelDifference-of-convex problemPrediction-error methodIdentification of structured state-space (gray-box) model is popular for modeling physical and network systems. Due to the non-convex nature of the gray-box identification problem, good initial parameter estimates are crucial...
Point cloud completion aims to generate a complete and high-fidelity point cloud from an initially incomplete and low-quality input. A prevalent strategy involves leveraging Transformer-based models to encode global features and facilitate the reconstruction process. However, the adoption of pooling opera...
Structured State Spaces for Sequence Modeling This repository provides the official implementations and experiments for models related to S4, including HiPPO, LSSL, SaShiMi, DSS, HTTYH, S4D, and S4ND. Project-specific information for each of these models, including overview of the source code and...
2009. Assessing threats to species-at-risk using stage-structured state-space models: mortality trends in skate populations. Ecol. Appl. 19: 1347-1364 CrossRef, Medline, ISI. Swain DP, Benoît HP, Hammill MO, McClelland G, Aubry... Swain,Douglas,P.,... - 《Canadian Journal of Fisherie...
We propose the Structured State Space sequence model (S4) based on a new parameterization for the SSM, and show that it can be computed much more efficiently than prior approaches while preserving their theoretical strengths. Our technique involves conditioning \( A \) with a low-rank ...
A method for robust eigenvalue location analysis of linear state-space models affected by structured real parametric perturbations is proposed. The approach, based on algebraic matrix properties, deals with state-space models in which system matrix entries are perturbed by polynomial functions of a set...
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Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have shown impressive results on image and waveform generation in continuous state spaces. Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs), diffusion-like generative models for discrete dat...