Summary of Enforcing Interpretability in Time Series Transformers: a Concept Bottleneck Framework, by Angela Van Sprang et al.
Enforcing Interpretability in Time Series Transformers: A Concept Bottleneck Framework
by Angela van Sprang, Erman Acar, Willem Zuidema
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 framework, based on Concept Bottleneck Models, enables interpretability for Transformer-based models used in long-term time series forecasting. By modifying the training objective to encourage representations similar to predefined interpretable concepts, the model’s performance remains mostly unaffected while its interpretability improves significantly. The framework is applied to the Autoformer model and analyzed for various benchmark tasks, demonstrating the ability to develop local and intervenable interpretations of the trained model. This work addresses a significant gap in research on Transformer-based models for long-term time series forecasting and has potential applications in domains such as climate modeling and finance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to make a type of artificial intelligence (AI) model more understandable. The AI model, called the Transformer, is really good at predicting what will happen in the future based on past data, but it’s hard to figure out why it made certain predictions. To solve this problem, the team created a framework that helps the Transformer develop explanations for its decisions. They tested their framework with different types of data and found that it worked well without sacrificing the model’s ability to make accurate predictions. This breakthrough has important implications for fields like climate modeling and finance, where being able to understand AI decision-making is crucial. |
Keywords
» Artificial intelligence » Time series » Transformer
