Fast and Flexible Temporal Point Processes with Triangular Maps
arXiv:2006.12631
Abstract
Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data. While such models are flexible, they are inherently sequential and therefore cannot benefit from the parallelism of modern hardware. By exploiting the recent developments in the field of normalizing flows, we design TriTPP -- a new class of non-recurrent TPP models, where both sampling and likelihood computation can be done in parallel. TriTPP matches the flexibility of RNN-based methods but permits orders of magnitude faster sampling. This enables us to use the new model for variational inference in continuous-time discrete-state systems. We demonstrate the advantages of the proposed framework on synthetic and real-world datasets.
References in corpus (16)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- WaveNet: A Generative Model for Raw Audio
- Categorical Reparameterization with Gumbel-Softmax
- Conditional Image Generation with PixelCNN Decoders
- Uncovering the Temporal Dynamics of Diffusion Networks
- MADE: Masked Autoencoder for Distribution Estimation
- Fast MCMC sampling for Markov jump processes and extensions
- Fully Neural Network based Model for General Temporal Point Processes
- Deep Reinforcement Learning of Marked Temporal Point Processes
- Sum-of-Squares Polynomial Flow
- Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data
- Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling
- Uncertainty on Asynchronous Time Event Prediction
- A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference
- Markov-Modulated Hawkes Processes for Sporadic and Bursty Event Occurrences
- Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers