Summary of Cumulative Distribution Function Based General Temporal Point Processes, by Maolin Wang et al.
Cumulative Distribution Function based General Temporal Point Processes
by Maolin Wang, Yu Pan, Zenglin Xu, Ruocheng Guo, Xiangyu Zhao, Wanyu Wang, Yiqi Wang, Zitao Liu, Langming Liu
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes a novel approach to Temporal Point Processes (TPPs) that leverages Cumulative Distribution Functions (CDFs) to improve the accuracy and adaptability of deep TPP models. The authors introduce the CuFun model, which employs a monotonic neural network to represent the CDF and utilizes past events as a scaling factor. This innovation addresses limitations in traditional TPP modeling, including simplified log-likelihood calculations, extended applicability beyond predefined density function forms, and improved capture of long-range temporal patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to understand event sequences using Temporal Point Processes (TPPs). It creates a special type of model called CuFun that uses something called a Cumulative Distribution Function (CDF) to help predict future events. This approach helps solve some big problems with traditional TPP models, like making predictions faster and more accurate. |
Keywords
* Artificial intelligence * Log likelihood * Neural network
