Latent Block-Diffusion Temporal Point Processes: A Semi-Autoregressive Framework for Asynchronous Event Sequence Generation
arXiv:2606.24982v1 Announce Type: new Abstract: Modeling and sampling from the underlying distribution of asynchronous event sequences are crucial in various real-world applications, including social networks, medical diagnosis, and financial transactions. Existing autoregressive methods suffer from error accumulation during multi-step generation, while non-autoregressive diffusion methods are typically limited to fixed-length output sequences. In this paper, we propose Latent Block-Diffusion Temporal Point Processes (LBDTPP), a novel semi-autoregressive TPP framework that introduces a latent
The continuous evolution of AI models demands more sophisticated methods for handling asynchronous, real-world data sequences, pushing research towards hybrid approaches that address limitations of existing methods.
This development proposes a new method for generating and analyzing event sequences, which is crucial for applications where events occur asynchronously and sequentially, impacting various sectors from social networks to finance and medicine.
The introduction of LBDTPP offers a semi-autoregressive alternative that mitigates error accumulation in multi-step generation and overcomes fixed-length limitations of many diffusion models for temporal point processes.
- · AI researchers
- · Temporal modeling practitioners
- · Financial modeling platforms
- · Healthcare diagnostics
- · Purely autoregressive temporal models
- · Fixed-length non-autoregressive diffusion models
Improved accuracy and flexibility in generating and analyzing complex, event-driven data.
Accelerated development of more robust AI agents capable of understanding and predicting dynamic real-world systems.
Enhanced automation and predictive capabilities in critical infrastructure sectors, potentially leading to more efficient resource allocation and risk management.
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