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Humans excel at lifelong learning, as the brain has evolved to be robust to distribution shifts and noise in our ever-changing environment.
Random sampling with a reservoir
Jeffrey S Vitter · 1985
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
James L McClelland, Bruce L McNaughton, and Randall C O’Reilly · 1995
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Is heterosynaptic modulation essential for stabilizing hebbian plasiticity and memory
Craig H Bailey, Maurizio Giustetto, Yan-You Huang, Robert D Hawkins, and Eric R Kandel · 2000
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Metalearning and neuromodulation
Kenji Doya · 2002
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Modulation of the rate of error-dependent learning by the statistical properties of the task
Maurice A Smith and Reza Shadmehr · 2004
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Sleep-dependent learning and memory consolidation
Matthew P Walker and Robert Stickgold · 2004
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Metaplasticity: tuning synapses and networks for plasticity
Wickliffe C Abraham · 2008
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Size of error affects cerebellar contributions to motor learning
Sarah E Criscimagna-Hemminger, Amy J Bastian, and Reza Shadmehr · 2010
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Sensitivity to prediction error in reach adaptation
Mollie K Marko, Adrian M Haith, Michelle D Harran, and Reza Shadmehr · 2012
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Environmental consistency determines the rate of motor adaptation
Luis Nicolas Gonzalez Castro, Alkis M Hadjiosif, Matthew A Hemphill, and Maurice A Smith · 2014
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A memory of errors in sensorimotor learning
David J Herzfeld, Pavan A Vaswani, Mollie K Marko, and Reza Shadmehr · 2014
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Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2014
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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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A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
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Neuroscience-inspired artificial intelligence
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick · 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
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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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Sandeep Madireddy, Angel Yanguas-Gil, and Prasanna Balaprakash · 2020
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Generalized class incremental learning
Fei Mi, Lingjing Kong, Tao Lin, Kaicheng Yu, and Boi Faltings · 2020
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Understanding the role of training regimes in continual learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, and Hassan Ghasemzadeh · 2020
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Learning fast, learning slow: A general continual learning method based on complementary learning system
Elahe Arani, Fahad Sarfraz, and Bahram Zonooz · 2021
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Towards robust evaluations of continual learning
Sebastian Farquhar and Yarin Gal · 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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Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2019
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Biologically inspired sleep algorithm for artificial neural networks
Giri P Krishnan, Timothy Tadros, Ramyaa Ramyaa, and Maxim Bazhenov · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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Three scenarios for continual learning
Gido M van de Ven and Andreas S Tolias · 2019
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New insights on reducing abrupt representation change in online continual learning
Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, and Eugene Belilovsky · 2021
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A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars · 2021
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Replay in deep learning: Current approaches and missing biological elements
Tyler L Hayes, Giri P Krishnan, Maxim Bazhenov, Hava T Siegelmann, Terrence J Sejnowski, and Christopher Kanan · 2021
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Dualnet: Continual learning, fast and slow
Quang Pham, Chenghao Liu, and Steven Hoi · 2021
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Biological underpinnings for lifelong learning machines
Dhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, et al · 2022
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Dualprompt: Complementary prompting for rehearsal-free continual learning
Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, et al · 2022
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