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Humans learn adaptively and efficiently throughout their lives.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael Mccloskey and Neil J. Cohen · 1989
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Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
R. Ratcliff · 1990
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Anthony Robins · 1995
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Alex Krizhevsky · 2009
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Principal Component Analysis
Hervé Abdi and Lynne J. Williams · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, T. Wang, A. Coates, Alessandro Bissacco, B. Wu, and A. Ng · 2011
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Notmnist dataset
Yaroslav Bulatov · 2011
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Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Matthew D. Zeiler and Rob Fergus · 2013
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Hongwei Ng and S. Winkler · 2014
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cuDNN: Efficient primitives for deep learning
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen. King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William J. Dally · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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iCaRL: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H. Lampert · 2016
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 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
Progress & Compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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Re-evaluating continual learning scenarios: A categorization and case for strong baselines
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Towards robust evaluations of continual learning
Sebastian Farquhar and Yarin Gal · 2018
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Generative replay with feedback connections as a general strategy for continual learning
Michiel van der Ven and Andreas S. Tolias · 2018
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Continual lifelong learning with neural networks: A review
German Ignacio Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and Stefan Wermter · 2019
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Learning without forgetting
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Song Han, Jeff Pool, Sharan Narang, Huizi Mao, Enhao Gong, Shijian Tang, Erich Elsen, Peter Vajda, Manohar Paluri, John Tran, Bryan Catanzaro, and William J. Dally · 2016
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Learning structured sparsity in deep neural networks
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Gradient episodic memory for continuum learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting
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Continual learning of context-dependent processing in neural networks
Guanxiong Zeng, Yang Chen, Bo Cui, and Shan Yu · 2019
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Continual learning with tiny episodic memories
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Gradient based sample selection for online continual learning
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Compacting, picking and growing for unforgetting continual learning
Ching-Yi Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen · 2019
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Random path selection for incremental learning
Jathushan Rajasegaran, Munawar Hayat, Salman Khan, Fahad Shahbaz Khan, and Ling Shao · 2019
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Sparse tensor core: Algorithm and hardware co-design for vector-wise sparse neural networks on modern gpus
Maohua Zhu, Tao Zhang, Zhenyu Gu, and Yuan Xie · 2019
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Improved schemes for episodic memory-based lifelong learning
Yunhui Guo, Mingrui Liu, Tianbao Yang, and Tajana Rosing · 2020
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Uncertainty-guided continual learning with bayesian neural networks
Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, and Marcus Rohrbach · 2020
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Adversarial continual learning
Sayna Ebrahimi, Franziska Meier, Roberto Calandra, Trevor Darrell, and Marcus Rohrbach · 2020
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Scalable and order-robust continual learning with additive parameter decomposition
Jaehong Yoon, Saehoon Kim, Eunho Yang, and Sung Ju Hwang · 2020
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Tree-CNN: A hierarchical deep convolutional neural network for incremental learning
Deboleena Roy, Priyadarshini Panda, and Kaushik Roy · 2020
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A low effort approach to structured CNN design using PCA
Isha Garg, Priyadarshini Panda, and Kaushik Roy · 2020
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A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, A. Leonardis, Gregory Slabaugh, and T. Tuytelaars · 2020
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