Conditional Generative Modeling for High-dimensional Marked Temporal Point Processes

• URL: http://arxiv.org/abs/2305.12569v3
• Date: Wed, 14 Feb 2024 18:42:30 GMT
• Title: Conditional Generative Modeling for High-dimensional Marked Temporal Point Processes
• Authors: Zheng Dong, Zekai Fan, Shixiang Zhu
• Abstract summary: We propose a novel event-generation framework for modeling point processes with high-dimensional marks. We use a conditional generator that takes the history of events as input and generates the high-quality subsequent event. Our numerical results demonstrate superior performance compared to other state-of-the-art baselines.
• Score: 9.141687745550481
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point processes offer a versatile framework for sequential event modeling. However, the computational challenges and constrained representational power of the existing point process models have impeded their potential for wider applications. This limitation becomes especially pronounced when dealing with event data that is associated with multi-dimensional or high-dimensional marks such as texts or images. To address this challenge, this study proposes a novel event-generation framework for modeling point processes with high-dimensional marks. We aim to capture the distribution of events without explicitly specifying the conditional intensity or probability density function. Instead, we use a conditional generator that takes the history of events as input and generates the high-quality subsequent event that is likely to occur given the prior observations. The proposed framework offers a host of benefits, including considerable representational power to capture intricate dynamics in multi- or even high-dimensional event space, as well as exceptional efficiency in learning the model and generating samples. Our numerical results demonstrate superior performance compared to other state-of-the-art baselines.

Related papers

• EventFlow: Forecasting Continuous-Time Event Data with Flow Matching [12.976042923229466]
  We propose EventFlow, a non-autoregressive generative model for temporal point processes. Our model builds on the flow matching framework in order to directly learn joint distributions over event times, side-stepping the autoregressive process.
  arXiv  Detail & Related papers  (2024-10-09T20:57:00Z)
• EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention [17.049035309926637]
  We present EtriCA, a novel neural generation model, which improves the relevance and coherence of the generated stories. We show that our model significantly outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-10-22T14:51:12Z)
• Avoiding Post-Processing with Event-Based Detection in Biomedical Signals [69.34035527763916]
  We propose an event-based modeling framework that directly works with events as learning targets. We show that event-based modeling (without post-processing) performs on par with or better than epoch-based modeling with extensive post-processing.
  arXiv  Detail & Related papers  (2022-09-22T13:44:13Z)
• CEP3: Community Event Prediction with Neural Point Process on Graph [59.434777403325604]
  We propose a novel model combining Graph Neural Networks and Marked Temporal Point Process (MTPP) Our experiments demonstrate the superior performance of our model in terms of both model accuracy and training efficiency.
  arXiv  Detail & Related papers  (2022-05-21T15:30:25Z)
• Counterfactual Temporal Point Processes [18.37409880250174]
  We develop a causal model of thinning for temporal point processes that builds upon the Gumbel-Max structural causal model. We then simulate counterfactual realizations of the temporal point process under a given alternative intensity function.
  arXiv  Detail & Related papers  (2021-11-15T08:46:25Z)
• Event Data Association via Robust Model Fitting for Event-based Object Tracking [66.05728523166755]
  We propose a novel Event Data Association (called EDA) approach to explicitly address the event association and fusion problem. The proposed EDA seeks for event trajectories that best fit the event data, in order to perform unifying data association and information fusion. The experimental results show the effectiveness of EDA under challenging scenarios, such as high speed, motion blur, and high dynamic range conditions.
  arXiv  Detail & Related papers  (2021-10-25T13:56:00Z)
• Neural Spectral Marked Point Processes [18.507050473968985]
  We introduce a novel and general neural network-based non-stationary influence kernel for handling complex discrete events. We demonstrate the superior performance of our proposed method compared with the state-of-the-art on synthetic and real data.
  arXiv  Detail & Related papers  (2021-06-20T23:00:37Z)
• Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation [51.29486247405601]
  We design a framework to generate counterfactuals for raw data instances with the proposed Attribute-Informed Perturbation (AIP) By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework.
  arXiv  Detail & Related papers  (2021-01-18T08:37:13Z)
• Team RUC_AIM3 Technical Report at Activitynet 2020 Task 2: Exploring Sequential Events Detection for Dense Video Captioning [63.91369308085091]
  We propose a novel and simple model for event sequence generation and explore temporal relationships of the event sequence in the video. The proposed model omits inefficient two-stage proposal generation and directly generates event boundaries conditioned on bi-directional temporal dependency in one pass. The overall system achieves state-of-the-art performance on the dense-captioning events in video task with 9.894 METEOR score on the challenge testing set.
  arXiv  Detail & Related papers  (2020-06-14T13:21:37Z)
• A Multi-Channel Neural Graphical Event Model with Negative Evidence [76.51278722190607]
  Event datasets are sequences of events of various types occurring irregularly over the time-line. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions.
  arXiv  Detail & Related papers  (2020-02-21T23:10:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.