Summary of Interactive Dense Pixel Visualizations For Time Series and Model Attribution Explanations, by Udo Schlegel et al.
Interactive dense pixel visualizations for time series and model attribution explanations
by Udo Schlegel, Daniel A. Keim
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper develops an interactive visual analytics approach called DAVOTS, designed to explore raw time series data, activations of neural networks, and attributions in a dense-pixel visualization. This allows users to gain insights into the data, models’ decisions, and explanations. The approach is tailored for domains with non-intelligible data, such as time series, where differences in applied metrics can be subtle. To facilitate exploration, the paper applies clustering approaches to the visualized data domains and presents ordering strategies for individual and combined data exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand how a machine learning model makes decisions based on time series data. The problem is that this type of data isn’t easy to visualize or make sense of. To solve this, researchers have developed an interactive tool called DAVOTS that helps users explore and understand the data, the model’s decisions, and why it made certain choices. This tool can be very useful for finding patterns in large datasets. |
Keywords
» Artificial intelligence » Clustering » Machine learning » Time series
