BROWSE
Detail
Journal Article
Burst and Memory-Aware Transformer: Capturing Temporal Heterogeneity
- Authors
- Byounghwa Lee, Jung-Hoon Lee, Sungyup Lee, Cheol Ho Kim
- Issue Date
- 2023-12
- Citation
- Frontiers in Computational Neuroscience, v.17, pp.1-15
- ISSN
- 1662-5188
- Publisher
- Frontiers Media S.A.
- Language
- English
- Type
- Journal Article
- Abstract
- Burst patterns, characterized by their temporal heterogeneity, have been observed across a wide range of domains, encompassing event sequences from neuronal firing to various facets of human activities. Recent research on predicting event sequences leveraged a Transformer based on the Hawkes process, incorporating a self-attention mechanism to capture long-term temporal dependencies. To effectively handle bursty temporal patterns, we propose a Burst and Memory-aware Transformer (BMT) model, designed to explicitly address temporal heterogeneity. The BMT model embeds the burstiness and memory coefficient into the self-attention module, enhancing the learning process with insights derived from the bursty patterns. Furthermore, we employed a novel loss function designed to optimize the burstiness and memory coefficient values, as well as their corresponding discretized one-hot vectors, both individually and jointly. Numerical experiments conducted on diverse synthetic and real-world datasets demonstrated the outstanding performance of the BMT model in terms of accurately predicting event times and intensity functions compared to existing models and control groups. In particular, the BMT model exhibits remarkable performance for temporally heterogeneous data, such as those with power-law inter-event time distributions. Our findings suggest that the incorporation of burst-related parameters assists the Transformer in comprehending heterogeneous event sequences, leading to an enhanced predictive performance.
- KSP Keywords
- Attention mechanism, Control groups, Event sequences, Hawkes Process, Heterogeneous Data, Human activity, Intensity functions, Learning Process, Memory coefficient, Neuronal Firing, Numerical experiments
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
