Summary of On Non-asymptotic Theory Of Recurrent Neural Networks in Temporal Point Processes, by Zhiheng Chen and Guanhua Fang and Wen Yu
On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes
by Zhiheng Chen, Guanhua Fang, Wen Yu
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 This paper develops a theoretical understanding of recurrent neural network (RNN)-based temporal point process (TPP) models, which have shown practical advantages over traditional parametric TPP models in modeling irregularly timed events across various domains. The authors establish excess risk bounds for RNN-TPPs under different well-known TPP settings and show that an RNN-TPP with four layers or less can achieve vanishing generalization errors. The paper’s technical contributions include characterizing the complexity of multi-layer RNNs, constructing tanh neural networks to approximate dynamic event intensity functions, and introducing a truncation technique to address unbounded event sequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research studies how well computer models, called recurrent neural networks (RNNs), can predict when things happen. For example, when will someone call or send an email? The RNNs are good at this job because they can learn patterns in the timing of events. But until now, scientists didn’t fully understand why these models work so well. This paper helps fill that gap by showing how to measure the quality of these predictions and how many layers (or steps) the model needs to be very accurate. |
Keywords
» Artificial intelligence » Generalization » Neural network » Rnn » Tanh
