Summary of Can Transformers In-context Learn Behavior Of a Linear Dynamical System?, by Usman Akram et al.
Can Transformers In-Context Learn Behavior of a Linear Dynamical System?
by Usman Akram, Haris Vikalo
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 Transformers can learn to track a random process when given observations of a related process and parameters of the dynamical system that relates them as context. In this study, we investigate whether transformers can approximate the Kalman filter in finite-dimensional state-space models with randomly sampled parameters provided along with observations generated by the linear dynamical system. We empirically verify that transformers learn to estimate hidden states and predict one-step ahead using a Kalman-like approach. Moreover, we find that the transformer’s performance remains accurate even when partial model parameters are withheld, effectively emulating operations of the Dual-Kalman filter. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers can be taught to follow patterns in random processes if given information about related processes and the rules behind them. This study looks at whether transformers can learn like a special kind of filter called the Kalman filter. The researchers gave transformers some clues about how things move and changed over time, along with observations that were generated by these movements. They found that the transformers learned to guess what was happening next and to figure out what was hidden from view. This is important because it means that we might be able to use these powerful machines to analyze complex data in the future. |
Keywords
» Artificial intelligence » Transformer
