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Continual Learning is a learning paradigm where learning systems are trained with sequential or streaming tasks.
Clustering to minimize the maximum intercluster distance
Gonzalez, T. F · 1985
Earlier work this paper cites.
Catastrophic forgetting, rehearsal and pseudorehearsal
Robins, A · 1995
Earlier work this paper cites.
Child: A first step towards continual learning
Ring, M. B · 1997
Earlier work this paper cites.
Laplace propagation
Smola, A. J., Vishwanathan, V., and Eskin, E · 2003
Earlier work this paper cites.
Variational inference for the indian buffet process
Doshi, F., Miller, K., Van Gael, J., and Teh, Y. W · 2009
Earlier work this paper cites.
A survey on transfer learning
Pan, S. J. and Yang, Q · 2009
Earlier work this paper cites.
The indian buffet process: An introduction and review
Griffiths, T. L. and Ghahramani, Z · 2011
Earlier work this paper cites.
Bayesian learning for neural networks , volume 118
Neal, R. M · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
Earlier work this paper cites.
Stochastic Structured Variational Inference
Hoffman, M. and Blei, D · 2015
Earlier work this paper cites.
Stick-Breaking Variational Autoencoders
Nalisnick, E. and Smyth, P · 2016
Earlier work this paper cites.
Variational inference: A review for statisticians
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D · 2017
Earlier work this paper cites.
Pathnet: evolution channels gradient descent in super neural networks
Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A. A., Pritzel, A., and Wierstra, D · 2017
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 2017
Cited alongside, same era.
Overcoming Catastrophic Forgetting by Incremental Moment Matching
Lee, S.-W., Kim, J.-H., Jun, J., Ha, J.-W., and Zhang, B.-T · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
Lopez-Paz, D. et al · 2017
Cited alongside, same era.
The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S · 2017
Cited alongside, same era.
Lifelong learning with dynamically expandable networks, 2018
Yoon, J., Yang, E., Lee, J., and Hwang, S. J · 2018
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Task agnostic continual learning using online variational bayes
Zeno, C., Golan, I., Hoffer, E., and Soudry, D · 2018
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Whai: Weibull hybrid autoencoding inference for deep topic modeling
Zhang, H., Chen, B., Guo, D., and Zhou, M · 2018
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Continual Learning with Adaptive Weights (CLAW)
Adel, T., Zhao, H., and Turner, R. E · 2019
Closest in time.
Continual learning via neural pruning
Golkar, S., Kagan, M., and Cho, K · 2019
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Don’t forget, there is more than forgetting: new metrics for continual learning
Díaz-Rodríguez, N., Lomonaco, V., Filliat, D., and Maltoni, D · 2018
Cited alongside, same era.
Comparing continual task learning in minds and machines
Flesch, T., Balaguer, J., Dekker, R., Nili, H., and Summerfield, C · 2018
Cited alongside, same era.
Structured variational learning of bayesian neural networks with horseshoe priors
Ghosh, S., Yao, J., and Doshi-Velez, F · 2018
Cited alongside, same era.
Overcoming catastrophic forgetting for continual learning via model adaptation
Hu, W., Lin, Z., Liu, B., Tao, C., Tao, Z., Ma, J., Zhao, D., and Yan, R · 2018
Cited alongside, same era.
Variational continual learning
Nguyen, C. V., Li, Y., Bui, T. D., and Turner, R. E · 2018
Cited alongside, same era.
Progress & Compress: A scalable framework for continual learning
Schwarz, J., Luketina, J., Czarnecki, W. M., Grabska-Barwinska, A., Whye Teh, Y., Pascanu, R., and Hadsell, R · 2018
Cited alongside, same era.
Closest in time.
Indian buffet neural networks for continual learning
Kessler, S., Nguyen, V., Zohren, S., and Roberts, S · 2019
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Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting
Li, X., Zhou, Y., Wu, T., Socher, R., and Xiong, C · 2019
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Nonparametric bayesian deep networks with local competition
Panousis, K., Chatzis, S., and Theodoridis, S · 2019
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Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., and Wermter, S · 2019
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Continual unsupervised representation learning
Rao, D., Visin, F., Rusu, A., Pascanu, R., Teh, Y. W., and Hadsell, R · 2019
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Unsupervised continual learning and self-taught associative memory hierarchies
Smith, J., Baer, S., Kira, Z., and Dovrolis, C · 2019
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Functional regularisation for continual learning using gaussian processes
Titsias, M. K., Schwarz, J., Matthews, A. G. d. G., Pascanu, R., and Teh, Y. W · 2019
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Variational russian roulette for deep bayesian nonparametrics
Xu, K., Srivastava, A., and Sutton, C · 2019
Closest in time.