Prompt-augmented Temporal Point Process for Streaming Event Sequence

• URL: http://arxiv.org/abs/2310.04993v2
• Date: Fri, 13 Oct 2023 08:37:29 GMT
• Title: Prompt-augmented Temporal Point Process for Streaming Event Sequence
• Authors: Siqiao Xue, Yan Wang, Zhixuan Chu, Xiaoming Shi, Caigao Jiang, Hongyan Hao, Gangwei Jiang, Xiaoyun Feng, James Y. Zhang, Jun Zhou
• Abstract summary: We present a novel framework for continuous monitoring of a Neural Temporal Point Processes (TPP) model. PromptTPP consistently achieves state-of-the-art performance across three real user behavior datasets.
• Score: 18.873915278172095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling continuous-time event sequences, such as user activities on the web and financial transactions. In real-world applications, event data is typically received in a \emph{streaming} manner, where the distribution of patterns may shift over time. Additionally, \emph{privacy and memory constraints} are commonly observed in practical scenarios, further compounding the challenges. Therefore, the continuous monitoring of a TPP to learn the streaming event sequence is an important yet under-explored problem. Our work paper addresses this challenge by adopting Continual Learning (CL), which makes the model capable of continuously learning a sequence of tasks without catastrophic forgetting under realistic constraints. Correspondingly, we propose a simple yet effective framework, PromptTPP\footnote{Our code is available at {\small \url{ https://github.com/yanyanSann/PromptTPP}}}, by integrating the base TPP with a continuous-time retrieval prompt pool. The prompts, small learnable parameters, are stored in a memory space and jointly optimized with the base TPP, ensuring that the model learns event streams sequentially without buffering past examples or task-specific attributes. We present a novel and realistic experimental setup for modeling event streams, where PromptTPP consistently achieves state-of-the-art performance across three real user behavior datasets.

Related papers

• TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models [0.0]
  Temporal point processes (TPPs) are widely used to model the timing and occurrence of events in domains such as social networks, transportation systems, and e-commerce. We introduce TPP-LLM, a novel framework that integrates large language models (LLMs) with TPPs to capture both the semantic and temporal aspects of event sequences.
  arXiv  Detail & Related papers  (2024-10-02T22:17:24Z)
• Cumulative Distribution Function based General Temporal Point Processes [49.758080415846884]
  CuFun model represents a novel approach to TPPs that revolves around the Cumulative Distribution Function (CDF) Our approach addresses several critical issues inherent in traditional TPP modeling. Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction.
  arXiv  Detail & Related papers  (2024-02-01T07:21:30Z)
• Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference [21.51753838306655]
  Temporal point processes(TPPs) are the standard method for modeling such. Existing TPP models have focused on the conditional distribution of future events instead of explicitly modeling event interactions, imposing challenges for event predictions. We propose a novel approach that leverages a Neural Inference (NRI) to learn a graph that infers interactions while simultaneously learning dynamics patterns from observational data.
  arXiv  Detail & Related papers  (2023-09-06T09:47:03Z)
• CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation [128.00940554196976]
  Vision-Language Continual Pretraining (VLCP) has shown impressive results on diverse downstream tasks by offline training on large-scale datasets. To support the study of Vision-Language Continual Pretraining (VLCP), we first contribute a comprehensive and unified benchmark dataset P9D. The data from each industry as an independent task supports continual learning and conforms to the real-world long-tail nature to simulate pretraining on web data.
  arXiv  Detail & Related papers  (2023-08-14T13:53:18Z)
• Self-regulating Prompts: Foundational Model Adaptation without Forgetting [112.66832145320434]
  We introduce a self-regularization framework for prompting called PromptSRC. PromptSRC guides the prompts to optimize for both task-specific and task-agnostic general representations.
  arXiv  Detail & Related papers  (2023-07-13T17:59:35Z)
• HyperHawkes: Hypernetwork based Neural Temporal Point Process [5.607676459156789]
  Temporal point process serves as an essential tool for modeling time-to-event data in continuous time space. It is not generalizable to predict events from unseen sequences in dynamic environment. We propose textitHyperHawkes, a hypernetwork based temporal point process framework.
  arXiv  Detail & Related papers  (2022-10-01T07:14:19Z)
• Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes [25.074394338483575]
  A large fraction of data generated via human activities can be represented as a sequence of events over a continuous-time. Deep learning models over these continuous-time event sequences is a non-trivial task. In this work, we provide a novel unsupervised model and inference method for learning MTPP in presence of event sequences with missing events.
  arXiv  Detail & Related papers  (2022-06-23T18:23:20Z)
• Temporal Context Aggregation Network for Temporal Action Proposal Refinement [93.03730692520999]
  Temporal action proposal generation is a challenging yet important task in the video understanding field. Current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval. We propose TCANet to generate high-quality action proposals through "local and global" temporal context aggregation.
  arXiv  Detail & Related papers  (2021-03-24T12:34:49Z)
• Boundary-sensitive Pre-training for Temporal Localization in Videos [124.40788524169668]
  We investigate model pre-training for temporal localization by introducing a novel boundary-sensitive pretext ( BSP) task. With the synthesized boundaries, BSP can be simply conducted via classifying the boundary types. Extensive experiments show that the proposed BSP is superior and complementary to the existing action classification based pre-training counterpart.
  arXiv  Detail & Related papers  (2020-11-21T17:46:24Z)
• 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)

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.