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Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Using semi-distributed representations to overcome catastrophic forgetting in connectionist networks
Robert M French · 1991
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Catastrophic forgetting, rehearsal and pseudorehearsal
Anthony Robins · 1995
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Existence and uniqueness results for neural network approximations
Robert C Williamson and Uwe Helmke · 1995
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The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex
Edmund T Rolls, Alessandro Treves, and Martin J Tovee · 1997
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Reactivation of hippocampal cell assemblies: effects of behavioral state, experience, and eeg dynamics
Hemant S Kudrimoti, Carol A Barnes, and Bruce L McNaughton · 1999
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An integrative theory of prefrontal cortex function
Earl K Miller and Jonathan D Cohen · 2001
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New experiences enhance coordinated neural activity in the hippocampus
Sen Cheng and Loren M Frank · 2008
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Internally generated reactivation of single neurons in human hippocampus during free recall
Hagar Gelbard-Sagiv, Roy Mukamel, Michal Harel, Rafael Malach, and Itzhak Fried · 2008
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Reactivation of experience-dependent cell assembly patterns in the hippocampus
Joseph O’Neill, Timothy J Senior, Kevin Allen, John R Huxter, and Jozsef Csicsvari · 2008
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Awake replay of remote experiences in the hippocampus
Mattias P Karlsson and Loren M Frank · 2009
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Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval
Margaret F Carr, Shantanu P Jadhav, and Loren M Frank · 2011
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 2015
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Mental imagery: functional mechanisms and clinical applications
Joel Pearson, Thomas Naselaris, Emily A Holmes, and Stephen M Kosslyn · 2015
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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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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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A smoothness constraint on the development of object recognition
Justin N Wood · 2016
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Deep generative dual memory network for continual learning
Nitin Kamra, Umang Gupta, and Yan Liu · 2017
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Towards robust evaluations of continual learning
Sebastian Farquhar and Yarin Gal · 2018
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Continual classification learning using generative models
Frantzeska Lavda, Jason Ramapuram, Magda Gregorova, and Alexandros Kalousis · 2018
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Prioritized memory access explains planning and hippocampal replay
Marcelo G Mattar and Nathaniel D Daw · 2018
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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 · 2018
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Surprise and destabilize: prediction error influences episodic memory reconsolidation
Alyssa H Sinclair and Morgan D Barense · 2018
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Fearnet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting by incremental moment matching
Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, and Byoung-Tak Zhang · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
Cited alongside, same era.
Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
Cited alongside, same era.
Later among the works it cites.
Generative replay with feedback connections as a general strategy for continual learning
Gido M van de Ven and Andreas S Tolias · 2018
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Risto Vuorio, Dong-Yeon Cho, Daejoong Kim, and Jiwon Kim · 2018
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Continual learning with tiny episodic memories
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato · 2019
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Generative models from the perspective of continual learning
Timothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz, Andrei Stoian, and David Filliat · 2019
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Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
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Sequential replay of nonspatial task states in the human hippocampus
Nicolas W Schuck and Yael Niv · 2019
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Three scenarios for continual learning
Gido M van de Ven and Andreas S Tolias · 2019
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Zhizhong Li, Jose M Alvarez, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2019
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Lifelong generative modeling
Jason Ramapuram, Magda Gregorova, and Alexandros Kalousis · 2020
Closest in time.
Deep learning needs a prefrontal cortex
Jacob Russin, Randall C O’Reilly, and Yoshua Bengio · 2020
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Brain-like replay for continual learning with artificial neural networks
Gido M van de Ven, Hava T Siegelmann, and Andreas S Tolias · 2020
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