Point Process Flows
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Point ProcessesSimilar Papers 제목 키워드 기반
Equivariant Normalizing Flows for Point Processes and Sets
A point process describes how random sets of exchangeable points are generated. The points usually influence the positions of each other via attractive and repulsive forces. To model this behavior, it is enough to transf…
Point ProcessesSelf-conditioned Flow Map Language Models via Fixed-point Flows
Self-conditioning is a core technique that enhances continuous flow-based language models, where the model learns to denoise generated text by conditioning on its own denoising estimate. While empirically successful, its…
One-Shot Generative Flows: Existence and Obstructions
We study dynamic measure transport for generative modeling, focusing on transport maps that connect a source measure $P_0$ to a target measure $P_1$ by integrating a velocity field of the form $v_t(x) = \mathbb{E}[\dot X…
An Eulerian Perspective on Straight-Line Sampling
We study dynamic measure transport for generative modeling: specifically, flows induced by stochastic processes that bridge a specified source and target distribution. The conditional expectation of the process' velocity…
PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows
Point cloud denoising aims to restore clean point clouds from raw observations corrupted by noise and outliers while preserving the fine-grained details. We present a novel deep learning-based denoising model, that incor…
Deep LearningDenoisingDisentanglement