# Visualizing RNN States with Predictive Semantic Encodings

arXiv: https://arxiv.org/abs/1908.00588 · HTML: https://mlsanity.com/paper/1908.00588/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [Visualizing and Explaining Language Models](https://mlsanity.com/paper/2205.10238/) (2022)
- [RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers](https://mlsanity.com/paper/2409.03164/) (2024)

## Abstract (arXiv)

Recurrent Neural Networks are an effective and prevalent tool used to model sequential data such as natural language text. However, their deep nature and massive number of parameters pose a challenge for those intending to study precisely how they work. We present a visual technique that gives a high level intuition behind the semantics of the hidden states within Recurrent Neural Networks. This semantic encoding allows for hidden states to be compared throughout the model independent of their internal details. The proposed technique is displayed in a proof of concept visualization tool which is demonstrated to visualize the natural language processing task of language modelling.