  Event sequences can be modeled by temporal point processes (TPPs) to capture
their asynchronous and probabilistic nature. We propose an intensity-free
framework that directly models the point process distribution by utilizing
normalizing flows. This approach is capable of capturing highly complex
temporal distributions and does not rely on restrictive parametric forms.
Comparisons with state-of-the-art baseline models on both synthetic and
challenging real-life datasets show that the proposed framework is effective at
modeling the stochasticity of discrete event sequences.
