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Spatiotemporal predictive learning (ST-PL) is a hotspot with numerous applications, such as object movement and meteorological prediction.
“Long short-term memory,”
S. Hochreiter and J. Schmidhuber, · 1997
Earlier work this paper cites.
“Convolutional lstm network: A machine learning approach for precipitation nowcasting,”
X. Shi, Z. Chen, H. Wang, et al., · 2015
Earlier work this paper cites.
“Scheduled sampling for sequence prediction with recurrent neural networks,”
S. Bengio, O. Vinyals, N. Jaitly, et al., · 2015
Earlier work this paper cites.
“Unsupervised learning of video representations using lstms,”
N. Srivastava, E. Mansimov, and R. Salakhutdinov, · 2015
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J. Ba, J. Kiros, and G. Hinton, · 2016
Earlier work this paper cites.
“Deep learning for precipitation nowcasting: A benchmark and A new model,”
X. Shi, Z. Gao, L. Lausen, et al., · 2017
Earlier work this paper cites.
“Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms,”
Y. Wang, M. Long, J. Wang, et al., · 2017
Cited alongside, same era.
“Fixing weight decay regularization in adam,”
I. Loshchilov and F. Hutter, · 2017
Cited alongside, same era.
“Predrnn++: Towards A resolution of the deep-in-time dilemma in spatiotemporal predictive learning,”
Y. Wang, Z. Gao, M. Long, et al., · 2018
Cited alongside, same era.
“Learning to decompose and disentangle representations for video prediction,”
J. Hsieh, B. Liu, D. Huang, et al., · 2018
Cited alongside, same era.
“Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics,”
Y. Wang, J. Zhang, H. Zhu, et al., · 2019
Cited alongside, same era.
“Self-attention convlstm for spatiotemporal prediction,”
Z. Lin, M. Li, Z. Zheng, et al., · 2020
Later among the works it cites.
“Geolocation accuracy assessment of himawari-8/ahi imagery for application to terrestrial monitoring,”
Y. Yamamoto, K. Ichii, A. Higuchi, et al., · 2020
Later among the works it cites.
“Efficient and information-preserving future frame prediction and beyond,”
W. Yu, Y. Lu, S. Easterbrook, et al., · 2020
Later among the works it cites.
“Disentangling physical dynamics from unknown factors for unsupervised video prediction,”
V. Guen and N. Thome, · 2020
Later among the works it cites.
“Predrnn: A recurrent neural network for spatiotemporal predictive learning,”
Y. Wang, H. Wu, J. Zhang, et al., · 2021
Closest in time.
“Pde-driven spatiotemporal disentanglement,”
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“Eidetic 3d LSTM: A model for video prediction and beyond,”
Y. Wang, L. Jiang, M. Yang, et al., · 2019
Cited alongside, same era.
J. Donà, J. Franceschi, S. Lamprier, et al., · 2021
Closest in time.