Preventing Conflicting Gradients in Neural Marked Temporal Point Processes

TL;DR

This paper identifies conflicting gradient issues in neural marked temporal point processes during joint training and proposes new parametrizations to separate task learning, improving model performance on real-world datasets.

Contribution

The paper introduces novel parametrizations for neural MTPP models that prevent conflicting gradients by separating task-specific training, enhancing learning stability and accuracy.

Findings

Conflicting gradients can degrade neural MTPP training.

Separating task modeling improves performance.

Experimental results show benefits on real-world datasets.

Abstract

Neural Marked Temporal Point Processes (MTPP) are flexible models to capture complex temporal inter-dependencies between labeled events. These models inherently learn two predictive distributions: one for the arrival times of events and another for the types of events, also known as marks. In this study, we demonstrate that learning a MTPP model can be framed as a two-task learning problem, where both tasks share a common set of trainable parameters that are optimized jointly. We show that this often leads to the emergence of conflicting gradients during training, where task-specific gradients are pointing in opposite directions. When such conflicts arise, following the average gradient can be detrimental to the learning of each individual tasks, resulting in overall degraded performance. To overcome this issue, we introduce novel parametrizations for neural MTPP models that allow for separate modeling and training of each task, effectively avoiding the problem of conflicting gradients. Through experiments on multiple real-world event sequence datasets, we demonstrate the benefits of our framework compared to the original model formulations.