# Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations

arXiv: https://arxiv.org/abs/1911.00756 · HTML: https://mlsanity.com/paper/1911.00756/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2019.

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

- [Information is Power: Intrinsic Control via Information Capture](https://mlsanity.com/paper/2112.03899/) (2021)

## Abstract (arXiv)

Learning a model of dynamics from high-dimensional images can be a core ingredient for success in many applications across different domains, especially in sequential decision making. However, currently prevailing methods based on latent-variable models are limited to working with low resolution images only. In this work, we show that some of the issues with using high-dimensional observations arise from the discrepancy between the dimensionality of the latent and observable space, and propose solutions to overcome them.