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Continual learning studies agents that learn from streams of tasks without forgetting previous ones while adapting to new ones.
Learning to remember: A synaptic plasticity driven framework for continual learning
Ostapenko, O., Puscas, M. M., Klein, T., Jähnichen, P., and Nabi, M. (2019) · 1904
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Three scenarios for continual learning
van de Ven, G. M. and Tolias, A. S. (2019) · 1904
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Task agnostic continual learning via meta learning
He, X., Sygnowski, J., Galashov, A., Rusu, A. A., Teh, Y. W., and Pascanu, R. (2019) · 1906
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Continual learning for robotics
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., and Díaz-Rodríguez, N. (2019b) · 1907
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Fine-grained continual learning
Lomonaco, V., Maltoni, D., and Pellegrini, L. (2019) · 1907
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Continual learning: A comparative study on how to defy forgetting in classification tasks
De Lange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., and Tuytelaars, T. (2019) · 1909
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Rapid learning or feature reuse? towards understanding the effectiveness of maml
Raghu, A., Raghu, M., Bengio, S., and Vinyals, O. (2019) · 1909
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Online learned continual compression with adaptive quantization modules
Caccia, L., Belilovsky, E., Caccia, M., and Pineau, J. (2019) · 1911
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Continuous meta-learning without tasks
Harrison, J., Sharma, A., Finn, C., and Pavone, M. (2019) · 1912
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Regularization shortcomings for continual learning
Lesort, T., Stoian, A., and Filliat, D. (2019c) · 1912
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Approximation to bayes risk in repeated play
Hannan, J. (1957) · 1957
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State of the art—a survey of partially observable markov decision processes: theory, models, and algorithms
Monahan, G. E. (1982) · 1982
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Bandit problems: sequential allocation of experiments (Monographs on statistics and applied probability)
Berry, D. A. and Fristedt, B. (1985) · 1985
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Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Schmidhuber, J. (1987) · 1987
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Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M. and Cohen, N. J. (1989) · 1989
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A tutorial on hidden markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · 1989
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Foundations of learning in autonomous agents
Kaelbling, L. P. (1991) · 1991
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Learning in embedded systems
Kaelbling, L. P. (1993) · 1993
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Lifelong robot learning
Thrun, S. and Mitchell, T. M. (1995) · 1995
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Reinforcement learning: A survey
Kaelbling, L. P., Littman, M. L., and Moore, A. W. (1996) · 1996
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Planning and acting in partially observable stochastic domains
Kaelbling, L. P., Littman, M. L., and Cassandra, A. R. (1998) · 1998
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Hidden-mode markov decision processes for nonstationary sequential decision making
Choi, S. P., Yeung, D.-Y., and Zhang, N. L. (2000) · 2000
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An introduction to hidden markov models and bayesian networks
Ghahramani, Z. (2001) · 2001
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Beaulieu, S., Frati, L., Miconi, T., Lehman, J., Stanley, K. O., Clune, J., and Cheney, N. (2020) · 2002
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Defining benchmarks for continual few-shot learning
Antoniou, A., Patacchiola, M., Ochal, M., and Storkey, A. (2020) · 2004
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
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Mundt, M., Hong, Y. W., Pliushch, I., and Ramesh, V. (2020) · 2009
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MNIST handwritten digit database
LeCun, Y. and Cortes, C. (2010) · 2010
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Practical variational inference for neural networks
Graves, A. (2011) · 2011
Cited alongside, same era.
Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y. (2012) · 2012
Cited alongside, same era.
An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A., and Bengio, Y. (2013) · 2013
Cited alongside, same era.
A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A. (2014) · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
Cited alongside, same era.
Session-based recommendations with recurrent neural networks
Variational continual learning
Nguyen, C. V., Li, Y., Bui, T. D., and Turner, R. E. (2018) · 2018
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Openai five
OpenAI (2018) · 2018
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Tadam: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B., López, P. R., and Lacoste, A. (2018) · 2018
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Meta-learning for semi-supervised few-shot classification
Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., and Zemel, R. S. (2018) · 2018
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Learning to learn without forgetting by maximizing transfer and minimizing interference
Riemer, M., Cases, I., Ajemian, R., Liu, M., Rish, I., Tu, Y., and Tesauro, G. (2018) · 2018
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Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. (2015) · 2015
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2015) · 2015
Cited alongside, same era.
Short-term plasticity as cause–effect hypothesis testing in distal reward learning
Soltoggio, A. (2015) · 2015
Cited alongside, same era.
Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H. (2016) · 2016
Cited alongside, same era.
Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R. (2016) · 2016
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · 2016
Cited alongside, same era.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al. (2016) · 2016
Cited alongside, same era.
Schwarz, J., Luketina, J., Czarnecki, W. M., Grabska-Barwinska, A., Teh, Y. W., Pascanu, R., and Hadsell, R. (2018) · 2018
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Overcoming catastrophic forgetting with hard attention to the task
Serrà, J., Surís, D., Miron, M., and Karatzoglou, A. (2018) · 2018
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Learning to compare: Relation network for few-shot learning
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H., and Hospedales, T. M. (2018) · 2018
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Vuorio, R., Cho, D.-Y., Kim, D., and Kim, J. (2018) · 2018
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Reinforced continual learning
Xu, J. and Zhu, Z. (2018) · 2018
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Task agnostic continual learning using online variational bayes
Zeno, C., Golan, I., Hoffer, E., and Soudry, D. (2018) · 2018
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Task-free continual learning
Aljundi, R., Kelchtermans, K., and Tuytelaars, T. (2019b) · 2019
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Efficient lifelong learning with A-GEM
Chaudhry, A., Ranzato, M., Rohrbach, M., and Elhoseiny, M. (2019) · 2019
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Online meta-learning
Finn, C., Rajeswaran, A., Kakade, S., and Levine, S. (2019) · 2019
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Meta-learning representations for continual learning
Javed, K. and White, M. (2019) · 2019
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Reconciling meta-learning and continual learning with online mixtures of tasks
Jerfel, G., Grant, E., Griffiths, T., and Heller, K. A. (2019) · 2019
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Continual learning: A comparative study on how to defy forgetting in classification tasks
Lange, M. D., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., and Tuytelaars, T. (2019) · 2019
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Learning from the past: Continual meta-learning via bayesian graph modeling
Luo, Y., Huang, Z., Zhang, Z., Wang, Z., Baktashmotlagh, M., and Yang, Y. (2019) · 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) · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · 2019
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Experience replay for continual learning
Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T., and Wayne, G. (2019) · 2019
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Session-based social recommendation via dynamic graph attention networks
Song, W., Xiao, Z., Wang, Y., Charlin, L., Zhang, M., and Tang, J. (2019) · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019) · 2019
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Yu, T., Quillen, D., He, Z., Julian, R. R., Hausman, K., Finn, C., and Levine, S. (2019) · 2019
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Rlbench: The robot learning benchmark & learning environment
James, S. W., Ma, Z., Arrojo, D. R., and Davison, A. J. (2020) · 2020
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Synbols: Probing learning algorithms with synthetic datasets
Lacoste, A., Rodríguez, P., Branchaud-Charron, F., Atighehchian, P., Caccia, M., Laradji, I., Drouin, A., Craddock, M., Charlin, L., and Vázquez, D. (2020) · 2020
Closest in time.
Embedding propagation: Smoother manifold for few-shot classification
Rodríguez, P., Laradji, I., Drouin, A., and Lacoste, A. (2020) · 2020
Closest in time.