Summary of Zero-shot Imputation with Foundation Inference Models For Dynamical Systems, by Patrick Seifner et al.
Zero-shot Imputation with Foundation Inference Models for Dynamical Systems
by Patrick Seifner, Kostadin Cvejoski, Antonia Körner, Ramsés J. Sánchez
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Dynamical Systems (math.DS)
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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 for zero-shot time series imputation leverages ordinary differential equations (ODEs) to model natural and social phenomena. Building upon amortized inference and neural operators, it combines a broad probability distribution over ODE solutions with a neural recognition model. This hybrid approach generates synthetic data and trains offline to map noisy and sparse observations onto initial conditions and time derivatives of the ODE solutions. The framework demonstrates zero-shot imputation across 63 distinct time series and 10 different settings, often outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to fill in missing data points in time series. They use special equations called ordinary differential equations (ODEs) that describe how things change over time. The method involves creating a large dataset with synthetic ODE solutions and training a neural network to recognize patterns in the data. This allows the model to impute missing values without needing any more information about the specific context. The approach was tested on 63 different types of data and performed well, often better than other methods. |
Keywords
* Artificial intelligence * Inference * Neural network * Probability * Synthetic data * Time series * Zero shot
