Review for NeurIPS paper: Fast and Flexible Temporal Point Processes with Triangular Maps

Summary and Contributions: This work first proposes a new parametrization for several classic temporal point processes (TPPs), which enables efficient parallel likelihood computation and sampling. TPP allows to naturally handle data that consists of variable-number events in continuous time. These classic TTP models with existing parametrization was inherently sequential. Next, the authors proposed a new class of non-recurrent TPP models, namely TriTPP, where both sampling and likelihood computation can be done in parallel. TPP models combined with recurrent neural networks provide a highly flexible powerful framework, but still remain sequential, making TPPs poorly suited for sampling.