Summary of Eventflow: Forecasting Continuous-time Event Data with Flow Matching, by Gavin Kerrigan et al.
EventFlow: Forecasting Continuous-Time Event Data with Flow Matching
by Gavin Kerrigan, Kai Nelson, Padhraic Smyth
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A novel generative model called EventFlow is proposed for modeling continuous-time event sequences. Traditional autoregressive approaches can be limited in forecasting longer horizons due to cascading errors. The new model uses the flow matching framework to directly learn joint distributions over event times, bypassing the autoregressive process. This likelihood-free model is easy to implement and sample from, and outperforms or matches state-of-the-art models on standard benchmarks for unconditional and conditional generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EventFlow is a new way to predict when events will happen. Right now, we use neural networks to guess the time of the next event based on past events. But this method can make mistakes when predicting far into the future. EventFlow is different because it directly learns how events are related in a way that doesn’t rely on guessing the next event. This makes it better at predicting what will happen in the long run. |
Keywords
» Artificial intelligence » Autoregressive » Generative model » Likelihood
