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Spatiotemporal forecasting tasks, such as traffic flow, combustion dynamics, and weather forecasting, often require complex models that suffer from low training efficiency and high memory consumption.
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
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Estimating low-frequency variability and trends in atmospheric temperature using era-interim
AJ Simmons, P Poli, DP Dee, P Berrisford, H Hersbach, S Kobayashi, and C Peubey · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton · 2015
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U-net: Convolutional networks for biomedical image segmentation
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Convolutional lstm network: A machine learning approach for precipitation nowcasting, 2015
Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai kin Wong, and Wang chun Woo · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms
Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, and Philip S Yu · 2017
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Deep learning for spatio-temporal modeling: dynamic traffic flows and high frequency trading
Matthew F Dixon, Nicholas G Polson, and Vadim O Sokolov · 2019
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Knowledge transfer via distillation of activation boundaries formed by hidden neurons
Byeongho Heo, Minsik Lee, Sangdoo Yun, and Jin Young Choi · 2019
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Urban traffic prediction from spatio-temporal data using deep meta learning
Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, and Junbo Zhang · 2019
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On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
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Agree to disagree: Adaptive ensemble knowledge distillation in gradient space
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Fourier neural operator for parametric partial differential equations
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High-frequency component helps explain the generalization of convolutional neural networks
Haohan Wang, Xindi Wu, Zeyi Huang, and Eric P Xing · 2020
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Dissecting the high-frequency bias in convolutional neural networks
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Choose a transformer: Fourier or galerkin
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Mingi Ji, Byeongho Heo, and Sungrae Park · 2021
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Selective frequency network for image restoration
Yuning Cui, Yi Tao, Zhenshan Bing, Wenqi Ren, Xinwei Gao, Xiaochun Cao, Kai Huang, and Alois Knoll · 2023
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Curriculum temperature for knowledge distillation
Zheng Li, Xiang Li, Lingfeng Yang, Borui Zhao, Renjie Song, Lei Luo, Jun Li, and Jian Yang · 2023
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Online knowledge distillation via mutual contrastive learning for visual recognition
Chuanguang Yang, Zhulin An, Helong Zhou, Fuzhen Zhuang, Yongjun Xu, and Qian Zhang · 2023
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Skilful nowcasting of extreme precipitation with nowcastnet
Yuchen Zhang, Mingsheng Long, Kaiyuan Chen, Lanxiang Xing, Ronghua Jin, Michael I Jordan, and Jianmin Wang · 2023
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Knowledge distillation on spatial-temporal graph convolutional network for traffic prediction
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Machine learning for geographically differentiated climate change mitigation in urban areas
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Understanding depthwise separable convolutions and the efficiency of mobilenets
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Frequency attention for knowledge distillation
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Clip-kd: An empirical study of clip model distillation
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Decoupling dark knowledge via block-wise logit distillation for feature-level alignment
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Freekd: Knowledge distillation via semantic frequency prompt
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Neuralom: Neural ocean model for subseasonal-to-seasonal simulation
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Multi-teacher knowledge distillation with reinforcement learning for visual recognition
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