# Variational Hyper RNN for Sequence Modeling

arXiv: https://arxiv.org/abs/2002.10501 · HTML: https://mlsanity.com/paper/2002.10501/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Meta-Learning over Time for Destination Prediction Tasks](https://mlsanity.com/paper/2206.14801/) (2022)

## Abstract (arXiv)

In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence. Our method uses temporal latent variables to capture information about the underlying data pattern and dynamically decodes the latent information into modifications of weights of the base decoder and recurrent model. The efficacy of the proposed method is demonstrated on a range of synthetic and real-world sequential data that exhibit large scale variations, regime shifts, and complex dynamics.