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Continual learning systems will interact with humans, with each other, and with the physical world through time -- and continue to learn and adapt as they do.
Continual learning with tiny episodic memories
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Online continual learning with no task boundaries
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DBpedia: A nucleus for a web of open data
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On early stopping in gradient descent learning
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Toward an architecture for never-ending language learning
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Online learning and online convex optimization
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NEIL: Extracting visual knowledge from web data
X. Chen, A. Shrivastava, and A. Gupta · 2013
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YAGO2: A spatially and temporally enhanced knowledge base from wikipedia
J. Hoffart, F. M. Suchanek, K. Berberich, and G. Weikum · 2013
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Robobrain: Large-scale knowledge engine for robots
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Adam: A method for stochastic optimization
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Introduction to online convex optimization
E. Hazan · 2016
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Less-forgetting learning in deep neural networks
H. Jung, J. Ju, M. Jung, and J. Kim · 2016
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Learning without forgetting
Z. Li and D. Hoiem · 2016
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Progressive neural networks
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Google’s neural machine translation system: Bridging the gap between human and machine translation
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Expert gate: Lifelong learning with a network of experts
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Controlling linguistic style aspects in neural language generation
J. Ficler and Y. Goldberg · 2017
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A joint many-task model: Growing a neural network for multiple NLP tasks
K. Hashimoto, C. Xiong, Y. Tsuruoka, and R. Socher · 2017
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Efficient regret minimization in non-convex games
E. Hazan, K. Singh, and C. Zhang · 2017
Variational continual learning
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Online structured laplace approximations for overcoming catastrophic forgetting
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Progress & compress: A scalable framework for continual learning
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Overcoming catastrophic forgetting with hard attention to the task
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Uncertainty-based continual learning with adaptive regularization
H. Ahn, S. Cha, D. Lee, and T. Moon · 2019
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Facilitating Bayesian continual learning by natural gradients and Stein gradients
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Toward controlled generation of text
Z. Hu, Z. Yang, X. Liang, R. Salakhutdinov, and E. P. Xing · 2017
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al · 2017
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Gradient episodic memory for continual learning
D. Lopez-Paz and M. Ranzato · 2017
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Style transfer from non-parallel text by cross-alignment
T. Shen, T. Lei, R. Barzilay, and T. Jaakkola · 2017
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Continual learning with deep generative replay
H. Shin, J. K. Lee, J. Kim, and J. Kim · 2017
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Encoder based lifelong learning
A. R. Triki, R. Aljundi, M. B. Blaschko, and T. Tuytelaars · 2017
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Y. Chen, T. Diethe, and N. Lawrence · 2019
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Transformer-XL: Attentive language models beyond a fixed-length context
Z. Dai, Z. Yang, Y. Yang, J. Carbonell, Q. V. Le, and R. Salakhutdinov · 2019
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Parameter-efficient transfer learning for NLP
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CTRL: A conditional transformer language model for controllable generation
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Multiple-attribute text rewriting
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Continual learning: A comparative study on how to defy forgetting in classification tasks
M. D. Lange, R. Aljundi, M. Masana, S. Parisot, X. Jia, A. Leonardis, G. G. Slabaugh, and T. Tuytelaars · 2019
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Multi-task deep neural networks for natural language understanding
X. Liu, P. He, W. Chen, and J. Gao · 2019
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Continual lifelong learning with neural networks: A review
G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter · 2019
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Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2019
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Experience replay for continual learning
D. Rolnick, A. Ahuja, J. Schwarz, T. P. Lillicrap, and G. Wayne · 2019
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Improving and understanding variational continual learning
S. Swaroop, C. V. Nguyen, T. D. Bui, and R. E. Turner · 2019
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Language models are few-shot learners
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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Dark experience for general continual learning: a strong, simple baseline
P. Buzzega, M. Boschini, A. Porrello, D. Abati, and S. Calderara · 2020
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Plug and play language models: a simple approach to controlled text generation
S. Dathathri, A. Madotto, J. Lan, J. Hung, E. Frank, P. Molino, J. Yosinski, and R. Liu · 2020
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GDumb: A simple approach that questions our progress in continual learning
A. Prabhu, P. H. S. Torr, and P. K. Dokania · 2020
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Class-incremental learning via deep model consolidation
J. Zhang, J. Zhang, S. Ghosh, D. Li, S. Tasci, L. P. Heck, H. Zhang, and C. J. Kuo · 2020
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