Study of Opioid Crisis with SI Model Based on Data

Chen, Ziang; Qu, Leilei; Song, Qianqian

doi:10.1145/3349341.3349434

Cited by 1 publication

(3 citation statements)

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“…The pandemic problem has the nature property of the topology interaction. Infected population migrate to effect other region, this paper using the same approach in [2] to divide the dataset and doing the validation.…”

Section: Methodsmentioning

confidence: 99%

“…After built the local-temporal dependency, encoding the latent with the dual reparametrized variational mechanism. Let a posterior of latent vector z as latent p θ (z) with parameter θ, the denoising transition density q (z t | z 0 ) and reverse transition q(z 0 |z t ) can be obtained through (2). The variance convergence according:…”

Section: Dual Reparametrized Variationalmentioning

confidence: 99%

“…Multivariate Time Series forecasting, which extensively applied in multiple disciplines like fiance [25], economy [15], epidemic [2], self-driving [17], etc., have caught enormously research which summarized in [25] [15]. The most advanced results are mainly implemented leverage Deep Generative models, includes normalized flows [14], variational autoencoder (VAE) [11] [12], generative adversarial network (GAN) [5] etc.…”

Section: Introductionmentioning

confidence: 99%

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Dual reparametrized Variational Generative Model for Time-Series Forecasting

Chen¹

2022

Preprint

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This paper propose DualVDT, a generative model for Time-series forecasting. Introduced dual reparametrized variational mechanisms on variational autoencoder (VAE) to tighter the evidence lower bound (ELBO) of the model, prove the advance performance analytically. This mechanism leverage the latent score based generative model (SGM), explicitly denoising the perturbation accumulated on latent vector through reverse time stochastic differential equation and variational ancestral sampling. The posterior of denoised latent distribution fused with dual reparametrized variational density. The KL divergence in ELBO will reduce to reach the better results of the model. This paper also proposed a latent attention mechanisms to extract multivariate dependency explicitly. Build the local-temporal dependency simultaneously in factor wised through constructed local topology and temporal wised. The proven and experiment on multiple datasets illustrate, DualVDT, with a novel dual reparametrized structure, which denoise the latent perturbation through the reverse dynamics combining local-temporal inference, has the advanced performance both analytically and experimentally.Preprint. Under review.

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Section: Methodsmentioning

confidence: 99%

Section: Dual Reparametrized Variationalmentioning

confidence: 99%