Learning general temporal point processes based on dynamic weight generation

Author:

Liu Sishun,Li LiORCID

Funder

National Natural Science Foundation of China

National Science Foundation Project of CQ

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference50 articles.

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2. Mei H, Eisner J (2017) The neural hawkes process: A neurally self-modulating multivariate point process. In: Advances in neural information processing systems 30: Annual conference on neural information processing systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp 6754–6764

3. Zuo S, Jiang H, Li Z, Zhao T, Zha H (2020) Transformer Hawkes process. In: Proceedings of the 37th international conference on machine learning, Proceedings of machine learning research. PMLR, vol 119, pp 11692–11702

4. Li S, Xiao S, Zhu S, Du N, Xie Y, Song L (2018) Learning temporal point processes via reinforcement learning. In: Advances in neural information processing systems 31: Annual conference on neural information processing systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal Canada, pp 10804–10814

5. Guo R, Li J, Liu H (2018) INITIATOR: noise-contrastive estimation for marked temporal point process. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI 2018, July 13-19, 2018. ijcai.org, Stockholm, Sweden, pp 2191–2197

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