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The fast adaptation capability of deep neural networks in non-stationary environments is critical for online time series forecasting.
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What learning systems do intelligent agents need? complementary learning systems theory updated
Dharshan Kumaran, Demis Hassabis, and James L McClelland · 2016
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Online arima algorithms for time series prediction
Chenghao Liu, Steven CH Hoi, Peilin Zhao, and Jianling Sun · 2016
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Scaling memory-augmented neural networks with sparse reads and writes
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On quadratic penalties in elastic weight consolidation
Ferenc Huszár · 2017
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Herbert Jaeger · 2017
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Łukasz Kaiser, Ofir Nachum, Aurko Roy, and Samy Bengio · 2017
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Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2019
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Experience replay for continual learning
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Yulia Rubanova, Ricky TQ Chen, and David Duvenaud · 2019
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Dark experience for general continual learning: a strong, simple baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara · 2020
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2018
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Reply to huszár: The elastic weight consolidation penalty is empirically valid
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Learning from irregularly-sampled time series: A missing data perspective
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Online continual learning with natural distribution shifts: An empirical study with visual data
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
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Mitigating forgetting in online continual learning with neuron calibration
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Ts2vec: Towards universal representation of time series
Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
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