[18] Albert Gu, Karan Goel, Ankit Gupta, and Christopher Ré. On the parameterization and initialization of diagonal state space models. Advances in Neural Information Processing Systems, 35:35971-35983, 2022.
[19] Albert Gu, Karan Goel, and Christopher Ré. Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396, 2021.
[20] Jianyuan Guo, Kai Han, Han Wu, Yehui Tang, Xinghao Chen, Yunhe Wang, and Chang Xu. Cmt: Convolutional neural networks meet vision transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12175–12185, 2022.
[21] Vinayak Gupta and Srikanta Bedathur. Proactive: Self-attentive temporal point process flows for activity sequences. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 496–504, 2022.
[22] Alan G Hawkes. Spectra of some self-exciting and mutually exciting point processes. Biometrika, 58(1):83-90, 1971.
[23] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[24] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 1997.
[25] John J Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8):2554-2558, 1982.
[26] Shea Yon Jhin, Heejoo Shin, Sujie Kim, Seoyoung Hong, Minju Jo, Solhee Park, Noseong Park, Seungbeom Lee, Hwiyoung Maeng, and Seungmin Jeon. Attentive neural controlled differential equations for time-series classification and forecasting. Knowledge and Information Systems, pages 1-31, 2023.
[27] Junteng Jia and Austin R Benson. Neural jump stochastic differential equations. Advances in Neural Information Processing Systems, 32, 2019.
[28] Patrick Kidger. On neural differential equations. arXiv preprint arXiv:2202.02435, 2022.
[29] Patrick Kidger, Patric Bonnier, Imanol Perez Arribas, Christopher Salvi, and Terry Lyons. Deep signature transforms. Advances in Neural Information Processing Systems, 32, 2019.
[30] Patrick Kidger, James Morrill, James Foster, and Terry Lyons. Neural controlled differential equations for irregular time series. Advances in Neural Information Processing Systems, 33:6696-6707, 2020.
[31] Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya. Reformer: The efficient transformer. arXiv preprint arXiv:2001.04451, 2020.
[32] Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu. Modeling long-and short-term temporal patterns with deep neural networks. In The 41st international ACM SIGIR conference on research & development in information retrieval, pages 95-104, 2018.
[33] Jure Leskovec and Andrej Krevl. Snap datasets: Stanford large network dataset collection, 2014.
[34] Jiacheng Li, Yujie Wang, and Julian McAuley. Time interval aware self-attention for sequential recommendation. In Proceedings of the 13th international conference on web search and data mining, pages 322–330, 2020.
[35] Yan Li, Xinjiang Lu, Haoyi Xiong, Jian Tang, Jiantao Su, Bo Jin, and Dejing Dou. Towards long-term time-series forecasting: Feature, pattern, and distribution. arXiv preprint arXiv:2301.02068, 2023.
[36] Zachary C Lipton, David Kale, and Randall Wetzel. Directly modeling missing data in sequences with rnns: Improved classification of clinical time series. In Machine learning for healthcare conference, pages 253-270. PMLR, 2016.
[37] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692, 2019.