Summary of Detpp: Leveraging Object Detection For Robust Long-horizon Event Prediction, by Ivan Karpukhin et al.
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event Prediction
by Ivan Karpukhin, Andrey Savchenko
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 abstract proposes a novel approach called DeTPP (Detection-based Temporal Point Processes) to improve long-horizon event forecasting in various domains such as retail, finance, healthcare, and social networks. Traditional methods like Marked Temporal Point Processes (MTPP) often rely on autoregressive models but suffer from issues like converging to constant or repetitive outputs. DeTPP employs a unique matching-based loss function that selectively prioritizes reliably predictable events, improving the accuracy and diversity of predictions during inference. The proposed hybrid approach achieves up to a 77% relative improvement over existing methods and enhances next event prediction by up to 2.7% on a large transactional dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DeTPP is a new way to predict future events that works better than other methods. It’s like using a map to find the most important places first, rather than just looking at a bunch of random points. This helps DeTPP make more accurate predictions and avoid making the same mistake over and over again. The results show that DeTPP is really good at predicting what will happen next, and it’s also fast and efficient. |
Keywords
» Artificial intelligence » Autoregressive » Inference » Loss function
