Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics
This study examines the challenges of modeling complex and noisy data related to socioeconomic factors over time, with a focus on data from various districts in Odisha, India. Traditional time-series models struggle to capture both trends and variations together in this type of data. To tackle this, a Variational Neural Stochastic Differential Equation (V-NSDE) model is designed that combines the expressive dynamics of Neural SDEs with the generative capabilities of Variational Autoencoders (VAEs). This model uses an encoder and a decoder. The encoder takes the initial observations and district embeddings and translates them into a Gaussian distribution, which determines the mean and log-...
| Authors | Sandeep Kumar Samota, Reema Gupta, Snehashish Chakraverty |
|---|---|
| Primary category | cs.LG |
| Published | 1 April 2026 |
| Last updated at source | 7 August 2026 |
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