# Streaming and Learning the Personal Context

arXiv: https://arxiv.org/abs/2108.08234 · HTML: https://mlsanity.com/paper/2108.08234/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [Learning in the Wild with Incremental Skeptical Gaussian Processes](https://mlsanity.com/paper/2011.00928/) (2020)
- [Human-in-the-loop Handling of Knowledge Drift](https://mlsanity.com/paper/2103.14874/) (2021)

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

- [Lifelong Personal Context Recognition](https://mlsanity.com/paper/2205.10123/) (2022)

## Abstract (arXiv)

The representation of the personal context is complex and essential to improve the help machines can give to humans for making sense of the world, and the help humans can give to machines to improve their efficiency. We aim to design a novel model representation of the personal context and design a learning process for better integration with machine learning. We aim to implement these elements into a modern system architecture focus in real-life environments. Also, we show how our proposal can improve in specifically related work papers. Finally, we are moving forward with a better personal context representation with an improved model, the implementation of the learning process, and the architectural design of these components.